Spark2 Random Forests 随机森林

　　随机森林是决策树的集合。 随机森林结合许多决策树，以减少过度拟合的风险。 spark.ml实现支持随机森林，使用连续和分类特征，做二分类和多分类以及回归。

导入包

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

import org.apache.spark.sql.SparkSession import org.apache.spark.sql.Dataset import org.apache.spark.sql.Row import org.apache.spark.sql.DataFrame import org.apache.spark.sql.Column import org.apache.spark.sql.DataFrameReader import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder import org.apache.spark.sql.Encoder import org.apache.spark.sql.DataFrameStatFunctions import org.apache.spark.sql.functions._   import org.apache.spark.ml.linalg.Vectors import org.apache.spark.ml.feature.{ IndexToString, StringIndexer, VectorIndexer } import org.apache.spark.ml.feature.VectorAssembler import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.{ RandomForestClassificationModel, RandomForestClassifier } import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.tuning.{ ParamGridBuilder, CrossValidator }

 

导入源数据

+ View Code?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619

// affairs：一年来婚外情的频率   // gender：性别   // age：年龄   // yearsmarried：婚龄   // children：是否有小孩   // religiousness：宗教信仰程度（5分制，1分表示反对，5分表示非常信仰）  // education：学历  // occupation：职业（逆向编号的戈登7种分类）   // rating：对婚姻的自我评分（5分制，1表示非常不幸福，5表示非常幸福）   val spark = SparkSession.builder().appName("Spark Random Forest Classifier").config("spark.some.config.option", "some-value").getOrCreate()   // For implicit conversions like converting RDDs to DataFrames import spark.implicits._   val dataList: List[(Double, String, Double, Double, String, Double, Double, Double, Double)] = List(        (0, "male", 37, 10, "no", 3, 18, 7, 4),        (0, "female", 27, 4, "no", 4, 14, 6, 4),        (0, "female", 32, 15, "yes", 1, 12, 1, 4),        (0, "male", 57, 15, "yes", 5, 18, 6, 5),        (0, "male", 22, 0.75, "no", 2, 17, 6, 3),        (0, "female", 32, 1.5, "no", 2, 17, 5, 5),        (0, "female", 22, 0.75, "no", 2, 12, 1, 3),        (0, "male", 57, 15, "yes", 2, 14, 4, 4),        (0, "female", 32, 15, "yes", 4, 16, 1, 2),        (0, "male", 22, 1.5, "no", 4, 14, 4, 5),        (0, "male", 37, 15, "yes", 2, 20, 7, 2),        (0, "male", 27, 4, "yes", 4, 18, 6, 4),        (0, "male", 47, 15, "yes", 5, 17, 6, 4),        (0, "female", 22, 1.5, "no", 2, 17, 5, 4),        (0, "female", 27, 4, "no", 4, 14, 5, 4),        (0, "female", 37, 15, "yes", 1, 17, 5, 5),        (0, "female", 37, 15, "yes", 2, 18, 4, 3),        (0, "female", 22, 0.75, "no", 3, 16, 5, 4),        (0, "female", 22, 1.5, "no", 2, 16, 5, 5),        (0, "female", 27, 10, "yes", 2, 14, 1, 5),        (0, "female", 22, 1.5, "no", 2, 16, 5, 5),        (0, "female", 22, 1.5, "no", 2, 16, 5, 5),        (0, "female", 27, 10, "yes", 4, 16, 5, 4),        (0, "female", 32, 10, "yes", 3, 14, 1, 5),        (0, "male", 37, 4, "yes", 2, 20, 6, 4),        (0, "female", 22, 1.5, "no", 2, 18, 5, 5),        (0, "female", 27, 7, "no", 4, 16, 1, 5),        (0, "male", 42, 15, "yes", 5, 20, 6, 4),        (0, "male", 27, 4, "yes", 3, 16, 5, 5),        (0, "female", 27, 4, "yes", 3, 17, 5, 4),        (0, "male", 42, 15, "yes", 4, 20, 6, 3),        (0, "female", 22, 1.5, "no", 3, 16, 5, 5),        (0, "male", 27, 0.417, "no", 4, 17, 6, 4),        (0, "female", 42, 15, "yes", 5, 14, 5, 4),        (0, "male", 32, 4, "yes", 1, 18, 6, 4),        (0, "female", 22, 1.5, "no", 4, 16, 5, 3),        (0, "female", 42, 15, "yes", 3, 12, 1, 4),        (0, "female", 22, 4, "no", 4, 17, 5, 5),        (0, "male", 22, 1.5, "yes", 1, 14, 3, 5),        (0, "female", 22, 0.75, "no", 3, 16, 1, 5),        (0, "male", 32, 10, "yes", 5, 20, 6, 5),        (0, "male", 52, 15, "yes", 5, 18, 6, 3),        (0, "female", 22, 0.417, "no", 5, 14, 1, 4),        (0, "female", 27, 4, "yes", 2, 18, 6, 1),        (0, "female", 32, 7, "yes", 5, 17, 5, 3),        (0, "male", 22, 4, "no", 3, 16, 5, 5),        (0, "female", 27, 7, "yes", 4, 18, 6, 5),        (0, "female", 42, 15, "yes", 2, 18, 5, 4),        (0, "male", 27, 1.5, "yes", 4, 16, 3, 5),        (0, "male", 42, 15, "yes", 2, 20, 6, 4),        (0, "female", 22, 0.75, "no", 5, 14, 3, 5),        (0, "male", 32, 7, "yes", 2, 20, 6, 4),        (0, "male", 27, 4, "yes", 5, 20, 6, 5),        (0, "male", 27, 10, "yes", 4, 20, 6, 4),        (0, "male", 22, 4, "no", 1, 18, 5, 5),        (0, "female", 37, 15, "yes", 4, 14, 3, 1),        (0, "male", 22, 1.5, "yes", 5, 16, 4, 4),        (0, "female", 37, 15, "yes", 4, 17, 1, 5),        (0, "female", 27, 0.75, "no", 4, 17, 5, 4),        (0, "male", 32, 10, "yes", 4, 20, 6, 4),        (0, "female", 47, 15, "yes", 5, 14, 7, 2),        (0, "male", 37, 10, "yes", 3, 20, 6, 4),        (0, "female", 22, 0.75, "no", 2, 16, 5, 5),        (0, "male", 27, 4, "no", 2, 18, 4, 5),        (0, "male", 32, 7, "no", 4, 20, 6, 4),        (0, "male", 42, 15, "yes", 2, 17, 3, 5),        (0, "male", 37, 10, "yes", 4, 20, 6, 4),        (0, "female", 47, 15, "yes", 3, 17, 6, 5),        (0, "female", 22, 1.5, "no", 5, 16, 5, 5),        (0, "female", 27, 1.5, "no", 2, 16, 6, 4),        (0, "female", 27, 4, "no", 3, 17, 5, 5),        (0, "female", 32, 10, "yes", 5, 14, 4, 5),        (0, "female", 22, 0.125, "no", 2, 12, 5, 5),        (0, "male", 47, 15, "yes", 4, 14, 4, 3),        (0, "male", 32, 15, "yes", 1, 14, 5, 5),        (0, "male", 27, 7, "yes", 4, 16, 5, 5),        (0, "female", 22, 1.5, "yes", 3, 16, 5, 5),        (0, "male", 27, 4, "yes", 3, 17, 6, 5),        (0, "female", 22, 1.5, "no", 3, 16, 5, 5),        (0, "male", 57, 15, "yes", 2, 14, 7, 2),        (0, "male", 17.5, 1.5, "yes", 3, 18, 6, 5),        (0, "male", 57, 15, "yes", 4, 20, 6, 5),        (0, "female", 22, 0.75, "no", 2, 16, 3, 4),        (0, "male", 42, 4, "no", 4, 17, 3, 3),        (0, "female", 22, 1.5, "yes", 4, 12, 1, 5),        (0, "female", 22, 0.417, "no", 1, 17, 6, 4),        (0, "female", 32, 15, "yes", 4, 17, 5, 5),        (0, "female", 27, 1.5, "no", 3, 18, 5, 2),        (0, "female", 22, 1.5, "yes", 3, 14, 1, 5),        (0, "female", 37, 15, "yes", 3, 14, 1, 4),        (0, "female", 32, 15, "yes", 4, 14, 3, 4),        (0, "male", 37, 10, "yes", 2, 14, 5, 3),        (0, "male", 37, 10, "yes", 4, 16, 5, 4),        (0, "male", 57, 15, "yes", 5, 20, 5, 3),        (0, "male", 27, 0.417, "no", 1, 16, 3, 4),        (0, "female", 42, 15, "yes", 5, 14, 1, 5),        (0, "male", 57, 15, "yes", 3, 16, 6, 1),        (0, "male", 37, 10, "yes", 1, 16, 6, 4),        (0, "male", 37, 15, "yes", 3, 17, 5, 5),        (0, "male", 37, 15, "yes", 4, 20, 6, 5),        (0, "female", 27, 10, "yes", 5, 14, 1, 5),        (0, "male", 37, 10, "yes", 2, 18, 6, 4),        (0, "female", 22, 0.125, "no", 4, 12, 4, 5),        (0, "male", 57, 15, "yes", 5, 20, 6, 5),        (0, "female", 37, 15, "yes", 4, 18, 6, 4),        (0, "male", 22, 4, "yes", 4, 14, 6, 4),        (0, "male", 27, 7, "yes", 4, 18, 5, 4),        (0, "male", 57, 15, "yes", 4, 20, 5, 4),        (0, "male", 32, 15, "yes", 3, 14, 6, 3),        (0, "female", 22, 1.5, "no", 2, 14, 5, 4),        (0, "female", 32, 7, "yes", 4, 17, 1, 5),        (0, "female", 37, 15, "yes", 4, 17, 6, 5),        (0, "female", 32, 1.5, "no", 5, 18, 5, 5),        (0, "male", 42, 10, "yes", 5, 20, 7, 4),        (0, "female", 27, 7, "no", 3, 16, 5, 4),        (0, "male", 37, 15, "no", 4, 20, 6, 5),        (0, "male", 37, 15, "yes", 4, 14, 3, 2),        (0, "male", 32, 10, "no", 5, 18, 6, 4),        (0, "female", 22, 0.75, "no", 4, 16, 1, 5),        (0, "female", 27, 7, "yes", 4, 12, 2, 4),        (0, "female", 27, 7, "yes", 2, 16, 2, 5),        (0, "female", 42, 15, "yes", 5, 18, 5, 4),        (0, "male", 42, 15, "yes", 4, 17, 5, 3),        (0, "female", 27, 7, "yes", 2, 16, 1, 2),        (0, "female", 22, 1.5, "no", 3, 16, 5, 5),        (0, "male", 37, 15, "yes", 5, 20, 6, 5),        (0, "female", 22, 0.125, "no", 2, 14, 4, 5),        (0, "male", 27, 1.5, "no", 4, 16, 5, 5),        (0, "male", 32, 1.5, "no", 2, 18, 6, 5),        (0, "male", 27, 1.5, "no", 2, 17, 6, 5),        (0, "female", 27, 10, "yes", 4, 16, 1, 3),        (0, "male", 42, 15, "yes", 4, 18, 6, 5),        (0, "female", 27, 1.5, "no", 2, 16, 6, 5),        (0, "male", 27, 4, "no", 2, 18, 6, 3),        (0, "female", 32, 10, "yes", 3, 14, 5, 3),        (0, "female", 32, 15, "yes", 3, 18, 5, 4),        (0, "female", 22, 0.75, "no", 2, 18, 6, 5),        (0, "female", 37, 15, "yes", 2, 16, 1, 4),        (0, "male", 27, 4, "yes", 4, 20, 5, 5),        (0, "male", 27, 4, "no", 1, 20, 5, 4),        (0, "female", 27, 10, "yes", 2, 12, 1, 4),        (0, "female", 32, 15, "yes", 5, 18, 6, 4),        (0, "male", 27, 7, "yes", 5, 12, 5, 3),        (0, "male", 52, 15, "yes", 2, 18, 5, 4),        (0, "male", 27, 4, "no", 3, 20, 6, 3),        (0, "male", 37, 4, "yes", 1, 18, 5, 4),        (0, "male", 27, 4, "yes", 4, 14, 5, 4),        (0, "female", 52, 15, "yes", 5, 12, 1, 3),        (0, "female", 57, 15, "yes", 4, 16, 6, 4),        (0, "male", 27, 7, "yes", 1, 16, 5, 4),        (0, "male", 37, 7, "yes", 4, 20, 6, 3),        (0, "male", 22, 0.75, "no", 2, 14, 4, 3),        (0, "male", 32, 4, "yes", 2, 18, 5, 3),        (0, "male", 37, 15, "yes", 4, 20, 6, 3),        (0, "male", 22, 0.75, "yes", 2, 14, 4, 3),        (0, "male", 42, 15, "yes", 4, 20, 6, 3),        (0, "female", 52, 15, "yes", 5, 17, 1, 1),        (0, "female", 37, 15, "yes", 4, 14, 1, 2),        (0, "male", 27, 7, "yes", 4, 14, 5, 3),        (0, "male", 32, 4, "yes", 2, 16, 5, 5),        (0, "female", 27, 4, "yes", 2, 18, 6, 5),        (0, "female", 27, 4, "yes", 2, 18, 5, 5),        (0, "male", 37, 15, "yes", 5, 18, 6, 5),        (0, "female", 47, 15, "yes", 5, 12, 5, 4),        (0, "female", 32, 10, "yes", 3, 17, 1, 4),        (0, "female", 27, 1.5, "yes", 4, 17, 1, 2),        (0, "female", 57, 15, "yes", 2, 18, 5, 2),        (0, "female", 22, 1.5, "no", 4, 14, 5, 4),        (0, "male", 42, 15, "yes", 3, 14, 3, 4),        (0, "male", 57, 15, "yes", 4, 9, 2, 2),        (0, "male", 57, 15, "yes", 4, 20, 6, 5),        (0, "female", 22, 0.125, "no", 4, 14, 4, 5),        (0, "female", 32, 10, "yes", 4, 14, 1, 5),        (0, "female", 42, 15, "yes", 3, 18, 5, 4),        (0, "female", 27, 1.5, "no", 2, 18, 6, 5),        (0, "male", 32, 0.125, "yes", 2, 18, 5, 2),        (0, "female", 27, 4, "no", 3, 16, 5, 4),        (0, "female", 27, 10, "yes", 2, 16, 1, 4),        (0, "female", 32, 7, "yes", 4, 16, 1, 3),        (0, "female", 37, 15, "yes", 4, 14, 5, 4),        (0, "female", 42, 15, "yes", 5, 17, 6, 2),        (0, "male", 32, 1.5, "yes", 4, 14, 6, 5),        (0, "female", 32, 4, "yes", 3, 17, 5, 3),        (0, "female", 37, 7, "no", 4, 18, 5, 5),        (0, "female", 22, 0.417, "yes", 3, 14, 3, 5),        (0, "female", 27, 7, "yes", 4, 14, 1, 5),        (0, "male", 27, 0.75, "no", 3, 16, 5, 5),        (0, "male", 27, 4, "yes", 2, 20, 5, 5),        (0, "male", 32, 10, "yes", 4, 16, 4, 5),        (0, "male", 32, 15, "yes", 1, 14, 5, 5),        (0, "male", 22, 0.75, "no", 3, 17, 4, 5),        (0, "female", 27, 7, "yes", 4, 17, 1, 4),        (0, "male", 27, 0.417, "yes", 4, 20, 5, 4),        (0, "male", 37, 15, "yes", 4, 20, 5, 4),        (0, "female", 37, 15, "yes", 2, 14, 1, 3),        (0, "male", 22, 4, "yes", 1, 18, 5, 4),        (0, "male", 37, 15, "yes", 4, 17, 5, 3),        (0, "female", 22, 1.5, "no", 2, 14, 4, 5),        (0, "male", 52, 15, "yes", 4, 14, 6, 2),        (0, "female", 22, 1.5, "no", 4, 17, 5, 5),        (0, "male", 32, 4, "yes", 5, 14, 3, 5),        (0, "male", 32, 4, "yes", 2, 14, 3, 5),        (0, "female", 22, 1.5, "no", 3, 16, 6, 5),        (0, "male", 27, 0.75, "no", 2, 18, 3, 3),        (0, "female", 22, 7, "yes", 2, 14, 5, 2),        (0, "female", 27, 0.75, "no", 2, 17, 5, 3),        (0, "female", 37, 15, "yes", 4, 12, 1, 2),        (0, "female", 22, 1.5, "no", 1, 14, 1, 5),        (0, "female", 37, 10, "no", 2, 12, 4, 4),        (0, "female", 37, 15, "yes", 4, 18, 5, 3),        (0, "female", 42, 15, "yes", 3, 12, 3, 3),        (0, "male", 22, 4, "no", 2, 18, 5, 5),        (0, "male", 52, 7, "yes", 2, 20, 6, 2),        (0, "male", 27, 0.75, "no", 2, 17, 5, 5),        (0, "female", 27, 4, "no", 2, 17, 4, 5),        (0, "male", 42, 1.5, "no", 5, 20, 6, 5),        (0, "male", 22, 1.5, "no", 4, 17, 6, 5),        (0, "male", 22, 4, "no", 4, 17, 5, 3),        (0, "female", 22, 4, "yes", 1, 14, 5, 4),        (0, "male", 37, 15, "yes", 5, 20, 4, 5),        (0, "female", 37, 10, "yes", 3, 16, 6, 3),        (0, "male", 42, 15, "yes", 4, 17, 6, 5),        (0, "female", 47, 15, "yes", 4, 17, 5, 5),        (0, "male", 22, 1.5, "no", 4, 16, 5, 4),        (0, "female", 32, 10, "yes", 3, 12, 1, 4),        (0, "female", 22, 7, "yes", 1, 14, 3, 5),        (0, "female", 32, 10, "yes", 4, 17, 5, 4),        (0, "male", 27, 1.5, "yes", 2, 16, 2, 4),        (0, "male", 37, 15, "yes", 4, 14, 5, 5),        (0, "male", 42, 4, "yes", 3, 14, 4, 5),        (0, "female", 37, 15, "yes", 5, 14, 5, 4),        (0, "female", 32, 7, "yes", 4, 17, 5, 5),        (0, "female", 42, 15, "yes", 4, 18, 6, 5),        (0, "male", 27, 4, "no", 4, 18, 6, 4),        (0, "male", 22, 0.75, "no", 4, 18, 6, 5),        (0, "male", 27, 4, "yes", 4, 14, 5, 3),        (0, "female", 22, 0.75, "no", 5, 18, 1, 5),        (0, "female", 52, 15, "yes", 5, 9, 5, 5),        (0, "male", 32, 10, "yes", 3, 14, 5, 5),        (0, "female", 37, 15, "yes", 4, 16, 4, 4),        (0, "male", 32, 7, "yes", 2, 20, 5, 4),        (0, "female", 42, 15, "yes", 3, 18, 1, 4),        (0, "male", 32, 15, "yes", 1, 16, 5, 5),        (0, "male", 27, 4, "yes", 3, 18, 5, 5),        (0, "female", 32, 15, "yes", 4, 12, 3, 4),        (0, "male", 22, 0.75, "yes", 3, 14, 2, 4),        (0, "female", 22, 1.5, "no", 3, 16, 5, 3),        (0, "female", 42, 15, "yes", 4, 14, 3, 5),        (0, "female", 52, 15, "yes", 3, 16, 5, 4),        (0, "male", 37, 15, "yes", 5, 20, 6, 4),        (0, "female", 47, 15, "yes", 4, 12, 2, 3),        (0, "male", 57, 15, "yes", 2, 20, 6, 4),        (0, "male", 32, 7, "yes", 4, 17, 5, 5),        (0, "female", 27, 7, "yes", 4, 17, 1, 4),        (0, "male", 22, 1.5, "no", 1, 18, 6, 5),        (0, "female", 22, 4, "yes", 3, 9, 1, 4),        (0, "female", 22, 1.5, "no", 2, 14, 1, 5),        (0, "male", 42, 15, "yes", 2, 20, 6, 4),        (0, "male", 57, 15, "yes", 4, 9, 2, 4),        (0, "female", 27, 7, "yes", 2, 18, 1, 5),        (0, "female", 22, 4, "yes", 3, 14, 1, 5),        (0, "male", 37, 15, "yes", 4, 14, 5, 3),        (0, "male", 32, 7, "yes", 1, 18, 6, 4),        (0, "female", 22, 1.5, "no", 2, 14, 5, 5),        (0, "female", 22, 1.5, "yes", 3, 12, 1, 3),        (0, "male", 52, 15, "yes", 2, 14, 5, 5),        (0, "female", 37, 15, "yes", 2, 14, 1, 1),        (0, "female", 32, 10, "yes", 2, 14, 5, 5),        (0, "male", 42, 15, "yes", 4, 20, 4, 5),        (0, "female", 27, 4, "yes", 3, 18, 4, 5),        (0, "male", 37, 15, "yes", 4, 20, 6, 5),        (0, "male", 27, 1.5, "no", 3, 18, 5, 5),        (0, "female", 22, 0.125, "no", 2, 16, 6, 3),        (0, "male", 32, 10, "yes", 2, 20, 6, 3),        (0, "female", 27, 4, "no", 4, 18, 5, 4),        (0, "female", 27, 7, "yes", 2, 12, 5, 1),        (0, "male", 32, 4, "yes", 5, 18, 6, 3),        (0, "female", 37, 15, "yes", 2, 17, 5, 5),        (0, "male", 47, 15, "no", 4, 20, 6, 4),        (0, "male", 27, 1.5, "no", 1, 18, 5, 5),        (0, "male", 37, 15, "yes", 4, 20, 6, 4),        (0, "female", 32, 15, "yes", 4, 18, 1, 4),        (0, "female", 32, 7, "yes", 4, 17, 5, 4),        (0, "female", 42, 15, "yes", 3, 14, 1, 3),        (0, "female", 27, 7, "yes", 3, 16, 1, 4),        (0, "male", 27, 1.5, "no", 3, 16, 4, 2),        (0, "male", 22, 1.5, "no", 3, 16, 3, 5),        (0, "male", 27, 4, "yes", 3, 16, 4, 2),        (0, "female", 27, 7, "yes", 3, 12, 1, 2),        (0, "female", 37, 15, "yes", 2, 18, 5, 4),        (0, "female", 37, 7, "yes", 3, 14, 4, 4),        (0, "male", 22, 1.5, "no", 2, 16, 5, 5),        (0, "male", 37, 15, "yes", 5, 20, 5, 4),        (0, "female", 22, 1.5, "no", 4, 16, 5, 3),        (0, "female", 32, 10, "yes", 4, 16, 1, 5),        (0, "male", 27, 4, "no", 2, 17, 5, 3),        (0, "female", 22, 0.417, "no", 4, 14, 5, 5),        (0, "female", 27, 4, "no", 2, 18, 5, 5),        (0, "male", 37, 15, "yes", 4, 18, 5, 3),        (0, "male", 37, 10, "yes", 5, 20, 7, 4),        (0, "female", 27, 7, "yes", 2, 14, 4, 2),        (0, "male", 32, 4, "yes", 2, 16, 5, 5),        (0, "male", 32, 4, "yes", 2, 16, 6, 4),        (0, "male", 22, 1.5, "no", 3, 18, 4, 5),        (0, "female", 22, 4, "yes", 4, 14, 3, 4),        (0, "female", 17.5, 0.75, "no", 2, 18, 5, 4),        (0, "male", 32, 10, "yes", 4, 20, 4, 5),        (0, "female", 32, 0.75, "no", 5, 14, 3, 3),        (0, "male", 37, 15, "yes", 4, 17, 5, 3),        (0, "male", 32, 4, "no", 3, 14, 4, 5),        (0, "female", 27, 1.5, "no", 2, 17, 3, 2),        (0, "female", 22, 7, "yes", 4, 14, 1, 5),        (0, "male", 47, 15, "yes", 5, 14, 6, 5),        (0, "male", 27, 4, "yes", 1, 16, 4, 4),        (0, "female", 37, 15, "yes", 5, 14, 1, 3),        (0, "male", 42, 4, "yes", 4, 18, 5, 5),        (0, "female", 32, 4, "yes", 2, 14, 1, 5),        (0, "male", 52, 15, "yes", 2, 14, 7, 4),        (0, "female", 22, 1.5, "no", 2, 16, 1, 4),        (0, "male", 52, 15, "yes", 4, 12, 2, 4),        (0, "female", 22, 0.417, "no", 3, 17, 1, 5),        (0, "female", 22, 1.5, "no", 2, 16, 5, 5),        (0, "male", 27, 4, "yes", 4, 20, 6, 4),        (0, "female", 32, 15, "yes", 4, 14, 1, 5),        (0, "female", 27, 1.5, "no", 2, 16, 3, 5),        (0, "male", 32, 4, "no", 1, 20, 6, 5),        (0, "male", 37, 15, "yes", 3, 20, 6, 4),        (0, "female", 32, 10, "no", 2, 16, 6, 5),        (0, "female", 32, 10, "yes", 5, 14, 5, 5),        (0, "male", 37, 1.5, "yes", 4, 18, 5, 3),        (0, "male", 32, 1.5, "no", 2, 18, 4, 4),        (0, "female", 32, 10, "yes", 4, 14, 1, 4),        (0, "female", 47, 15, "yes", 4, 18, 5, 4),        (0, "female", 27, 10, "yes", 5, 12, 1, 5),        (0, "male", 27, 4, "yes", 3, 16, 4, 5),        (0, "female", 37, 15, "yes", 4, 12, 4, 2),        (0, "female", 27, 0.75, "no", 4, 16, 5, 5),        (0, "female", 37, 15, "yes", 4, 16, 1, 5),        (0, "female", 32, 15, "yes", 3, 16, 1, 5),        (0, "female", 27, 10, "yes", 2, 16, 1, 5),        (0, "male", 27, 7, "no", 2, 20, 6, 5),        (0, "female", 37, 15, "yes", 2, 14, 1, 3),        (0, "male", 27, 1.5, "yes", 2, 17, 4, 4),        (0, "female", 22, 0.75, "yes", 2, 14, 1, 5),        (0, "male", 22, 4, "yes", 4, 14, 2, 4),        (0, "male", 42, 0.125, "no", 4, 17, 6, 4),        (0, "male", 27, 1.5, "yes", 4, 18, 6, 5),        (0, "male", 27, 7, "yes", 3, 16, 6, 3),        (0, "female", 52, 15, "yes", 4, 14, 1, 3),        (0, "male", 27, 1.5, "no", 5, 20, 5, 2),        (0, "female", 27, 1.5, "no", 2, 16, 5, 5),        (0, "female", 27, 1.5, "no", 3, 17, 5, 5),        (0, "male", 22, 0.125, "no", 5, 16, 4, 4),        (0, "female", 27, 4, "yes", 4, 16, 1, 5),        (0, "female", 27, 4, "yes", 4, 12, 1, 5),        (0, "female", 47, 15, "yes", 2, 14, 5, 5),        (0, "female", 32, 15, "yes", 3, 14, 5, 3),        (0, "male", 42, 7, "yes", 2, 16, 5, 5),        (0, "male", 22, 0.75, "no", 4, 16, 6, 4),        (0, "male", 27, 0.125, "no", 3, 20, 6, 5),        (0, "male", 32, 10, "yes", 3, 20, 6, 5),        (0, "female", 22, 0.417, "no", 5, 14, 4, 5),        (0, "female", 47, 15, "yes", 5, 14, 1, 4),        (0, "female", 32, 10, "yes", 3, 14, 1, 5),        (0, "male", 57, 15, "yes", 4, 17, 5, 5),        (0, "male", 27, 4, "yes", 3, 20, 6, 5),        (0, "female", 32, 7, "yes", 4, 17, 1, 5),        (0, "female", 37, 10, "yes", 4, 16, 1, 5),        (0, "female", 32, 10, "yes", 1, 18, 1, 4),        (0, "female", 22, 4, "no", 3, 14, 1, 4),        (0, "female", 27, 7, "yes", 4, 14, 3, 2),        (0, "male", 57, 15, "yes", 5, 18, 5, 2),        (0, "male", 32, 7, "yes", 2, 18, 5, 5),        (0, "female", 27, 1.5, "no", 4, 17, 1, 3),        (0, "male", 22, 1.5, "no", 4, 14, 5, 5),        (0, "female", 22, 1.5, "yes", 4, 14, 5, 4),        (0, "female", 32, 7, "yes", 3, 16, 1, 5),        (0, "female", 47, 15, "yes", 3, 16, 5, 4),        (0, "female", 22, 0.75, "no", 3, 16, 1, 5),        (0, "female", 22, 1.5, "yes", 2, 14, 5, 5),        (0, "female", 27, 4, "yes", 1, 16, 5, 5),        (0, "male", 52, 15, "yes", 4, 16, 5, 5),        (0, "male", 32, 10, "yes", 4, 20, 6, 5),        (0, "male", 47, 15, "yes", 4, 16, 6, 4),        (0, "female", 27, 7, "yes", 2, 14, 1, 2),        (0, "female", 22, 1.5, "no", 4, 14, 4, 5),        (0, "female", 32, 10, "yes", 2, 16, 5, 4),        (0, "female", 22, 0.75, "no", 2, 16, 5, 4),        (0, "female", 22, 1.5, "no", 2, 16, 5, 5),        (0, "female", 42, 15, "yes", 3, 18, 6, 4),        (0, "female", 27, 7, "yes", 5, 14, 4, 5),        (0, "male", 42, 15, "yes", 4, 16, 4, 4),        (0, "female", 57, 15, "yes", 3, 18, 5, 2),        (0, "male", 42, 15, "yes", 3, 18, 6, 2),        (0, "female", 32, 7, "yes", 2, 14, 1, 2),        (0, "male", 22, 4, "no", 5, 12, 4, 5),        (0, "female", 22, 1.5, "no", 1, 16, 6, 5),        (0, "female", 22, 0.75, "no", 1, 14, 4, 5),        (0, "female", 32, 15, "yes", 4, 12, 1, 5),        (0, "male", 22, 1.5, "no", 2, 18, 5, 3),        (0, "male", 27, 4, "yes", 5, 17, 2, 5),        (0, "female", 27, 4, "yes", 4, 12, 1, 5),        (0, "male", 42, 15, "yes", 5, 18, 5, 4),        (0, "male", 32, 1.5, "no", 2, 20, 7, 3),        (0, "male", 57, 15, "no", 4, 9, 3, 1),        (0, "male", 37, 7, "no", 4, 18, 5, 5),        (0, "male", 52, 15, "yes", 2, 17, 5, 4),        (0, "male", 47, 15, "yes", 4, 17, 6, 5),        (0, "female", 27, 7, "no", 2, 17, 5, 4),        (0, "female", 27, 7, "yes", 4, 14, 5, 5),        (0, "female", 22, 4, "no", 2, 14, 3, 3),        (0, "male", 37, 7, "yes", 2, 20, 6, 5),        (0, "male", 27, 7, "no", 4, 12, 4, 3),        (0, "male", 42, 10, "yes", 4, 18, 6, 4),        (0, "female", 22, 1.5, "no", 3, 14, 1, 5),        (0, "female", 22, 4, "yes", 2, 14, 1, 3),        (0, "female", 57, 15, "no", 4, 20, 6, 5),        (0, "male", 37, 15, "yes", 4, 14, 4, 3),        (0, "female", 27, 7, "yes", 3, 18, 5, 5),        (0, "female", 17.5, 10, "no", 4, 14, 4, 5),        (0, "male", 22, 4, "yes", 4, 16, 5, 5),        (0, "female", 27, 4, "yes", 2, 16, 1, 4),        (0, "female", 37, 15, "yes", 2, 14, 5, 1),        (0, "female", 22, 1.5, "no", 5, 14, 1, 4),        (0, "male", 27, 7, "yes", 2, 20, 5, 4),        (0, "male", 27, 4, "yes", 4, 14, 5, 5),        (0, "male", 22, 0.125, "no", 1, 16, 3, 5),        (0, "female", 27, 7, "yes", 4, 14, 1, 4),        (0, "female", 32, 15, "yes", 5, 16, 5, 3),        (0, "male", 32, 10, "yes", 4, 18, 5, 4),        (0, "female", 32, 15, "yes", 2, 14, 3, 4),        (0, "female", 22, 1.5, "no", 3, 17, 5, 5),        (0, "male", 27, 4, "yes", 4, 17, 4, 4),        (0, "female", 52, 15, "yes", 5, 14, 1, 5),        (0, "female", 27, 7, "yes", 2, 12, 1, 2),        (0, "female", 27, 7, "yes", 3, 12, 1, 4),        (0, "female", 42, 15, "yes", 2, 14, 1, 4),        (0, "female", 42, 15, "yes", 4, 14, 5, 4),        (0, "male", 27, 7, "yes", 4, 14, 3, 3),        (0, "male", 27, 7, "yes", 2, 20, 6, 2),        (0, "female", 42, 15, "yes", 3, 12, 3, 3),        (0, "male", 27, 4, "yes", 3, 16, 3, 5),        (0, "female", 27, 7, "yes", 3, 14, 1, 4),        (0, "female", 22, 1.5, "no", 2, 14, 4, 5),        (0, "female", 27, 4, "yes", 4, 14, 1, 4),        (0, "female", 22, 4, "no", 4, 14, 5, 5),        (0, "female", 22, 1.5, "no", 2, 16, 4, 5),        (0, "male", 47, 15, "no", 4, 14, 5, 4),        (0, "male", 37, 10, "yes", 2, 18, 6, 2),        (0, "male", 37, 15, "yes", 3, 17, 5, 4),        (0, "female", 27, 4, "yes", 2, 16, 1, 4),        (3, "male", 27, 1.5, "no", 3, 18, 4, 4),        (3, "female", 27, 4, "yes", 3, 17, 1, 5),        (7, "male", 37, 15, "yes", 5, 18, 6, 2),        (12, "female", 32, 10, "yes", 3, 17, 5, 2),        (1, "male", 22, 0.125, "no", 4, 16, 5, 5),        (1, "female", 22, 1.5, "yes", 2, 14, 1, 5),        (12, "male", 37, 15, "yes", 4, 14, 5, 2),        (7, "female", 22, 1.5, "no", 2, 14, 3, 4),        (2, "male", 37, 15, "yes", 2, 18, 6, 4),        (3, "female", 32, 15, "yes", 4, 12, 3, 2),        (1, "female", 37, 15, "yes", 4, 14, 4, 2),        (7, "female", 42, 15, "yes", 3, 17, 1, 4),        (12, "female", 42, 15, "yes", 5, 9, 4, 1),        (12, "male", 37, 10, "yes", 2, 20, 6, 2),        (12, "female", 32, 15, "yes", 3, 14, 1, 2),        (3, "male", 27, 4, "no", 1, 18, 6, 5),        (7, "male", 37, 10, "yes", 2, 18, 7, 3),        (7, "female", 27, 4, "no", 3, 17, 5, 5),        (1, "male", 42, 15, "yes", 4, 16, 5, 5),        (1, "female", 47, 15, "yes", 5, 14, 4, 5),        (7, "female", 27, 4, "yes", 3, 18, 5, 4),        (1, "female", 27, 7, "yes", 5, 14, 1, 4),        (12, "male", 27, 1.5, "yes", 3, 17, 5, 4),        (12, "female", 27, 7, "yes", 4, 14, 6, 2),        (3, "female", 42, 15, "yes", 4, 16, 5, 4),        (7, "female", 27, 10, "yes", 4, 12, 7, 3),        (1, "male", 27, 1.5, "no", 2, 18, 5, 2),        (1, "male", 32, 4, "no", 4, 20, 6, 4),        (1, "female", 27, 7, "yes", 3, 14, 1, 3),        (3, "female", 32, 10, "yes", 4, 14, 1, 4),        (3, "male", 27, 4, "yes", 2, 18, 7, 2),        (1, "female", 17.5, 0.75, "no", 5, 14, 4, 5),        (1, "female", 32, 10, "yes", 4, 18, 1, 5),        (7, "female", 32, 7, "yes", 2, 17, 6, 4),        (7, "male", 37, 15, "yes", 2, 20, 6, 4),        (7, "female", 37, 10, "no", 1, 20, 5, 3),        (12, "female", 32, 10, "yes", 2, 16, 5, 5),        (7, "male", 52, 15, "yes", 2, 20, 6, 4),        (7, "female", 42, 15, "yes", 1, 12, 1, 3),        (1, "male", 52, 15, "yes", 2, 20, 6, 3),        (2, "male", 37, 15, "yes", 3, 18, 6, 5),        (12, "female", 22, 4, "no", 3, 12, 3, 4),        (12, "male", 27, 7, "yes", 1, 18, 6, 2),        (1, "male", 27, 4, "yes", 3, 18, 5, 5),        (12, "male", 47, 15, "yes", 4, 17, 6, 5),        (12, "female", 42, 15, "yes", 4, 12, 1, 1),        (7, "male", 27, 4, "no", 3, 14, 3, 4),        (7, "female", 32, 7, "yes", 4, 18, 4, 5),        (1, "male", 32, 0.417, "yes", 3, 12, 3, 4),        (3, "male", 47, 15, "yes", 5, 16, 5, 4),        (12, "male", 37, 15, "yes", 2, 20, 5, 4),        (7, "male", 22, 4, "yes", 2, 17, 6, 4),        (1, "male", 27, 4, "no", 2, 14, 4, 5),        (7, "female", 52, 15, "yes", 5, 16, 1, 3),        (1, "male", 27, 4, "no", 3, 14, 3, 3),        (1, "female", 27, 10, "yes", 4, 16, 1, 4),        (1, "male", 32, 7, "yes", 3, 14, 7, 4),        (7, "male", 32, 7, "yes", 2, 18, 4, 1),        (3, "male", 22, 1.5, "no", 1, 14, 3, 2),        (7, "male", 22, 4, "yes", 3, 18, 6, 4),        (7, "male", 42, 15, "yes", 4, 20, 6, 4),        (2, "female", 57, 15, "yes", 1, 18, 5, 4),        (7, "female", 32, 4, "yes", 3, 18, 5, 2),        (1, "male", 27, 4, "yes", 1, 16, 4, 4),        (7, "male", 32, 7, "yes", 4, 16, 1, 4),        (2, "male", 57, 15, "yes", 1, 17, 4, 4),        (7, "female", 42, 15, "yes", 4, 14, 5, 2),        (7, "male", 37, 10, "yes", 1, 18, 5, 3),        (3, "male", 42, 15, "yes", 3, 17, 6, 1),        (1, "female", 52, 15, "yes", 3, 14, 4, 4),        (2, "female", 27, 7, "yes", 3, 17, 5, 3),        (12, "male", 32, 7, "yes", 2, 12, 4, 2),        (1, "male", 22, 4, "no", 4, 14, 2, 5),        (3, "male", 27, 7, "yes", 3, 18, 6, 4),        (12, "female", 37, 15, "yes", 1, 18, 5, 5),        (7, "female", 32, 15, "yes", 3, 17, 1, 3),        (7, "female", 27, 7, "no", 2, 17, 5, 5),        (1, "female", 32, 7, "yes", 3, 17, 5, 3),        (1, "male", 32, 1.5, "yes", 2, 14, 2, 4),        (12, "female", 42, 15, "yes", 4, 14, 1, 2),        (7, "male", 32, 10, "yes", 3, 14, 5, 4),        (7, "male", 37, 4, "yes", 1, 20, 6, 3),        (1, "female", 27, 4, "yes", 2, 16, 5, 3),        (12, "female", 42, 15, "yes", 3, 14, 4, 3),        (1, "male", 27, 10, "yes", 5, 20, 6, 5),        (12, "male", 37, 10, "yes", 2, 20, 6, 2),        (12, "female", 27, 7, "yes", 1, 14, 3, 3),        (3, "female", 27, 7, "yes", 4, 12, 1, 2),        (3, "male", 32, 10, "yes", 2, 14, 4, 4),        (12, "female", 17.5, 0.75, "yes", 2, 12, 1, 3),        (12, "female", 32, 15, "yes", 3, 18, 5, 4),        (2, "female", 22, 7, "no", 4, 14, 4, 3),        (1, "male", 32, 7, "yes", 4, 20, 6, 5),        (7, "male", 27, 4, "yes", 2, 18, 6, 2),        (1, "female", 22, 1.5, "yes", 5, 14, 5, 3),        (12, "female", 32, 15, "no", 3, 17, 5, 1),        (12, "female", 42, 15, "yes", 2, 12, 1, 2),        (7, "male", 42, 15, "yes", 3, 20, 5, 4),        (12, "male", 32, 10, "no", 2, 18, 4, 2),        (12, "female", 32, 15, "yes", 3, 9, 1, 1),        (7, "male", 57, 15, "yes", 5, 20, 4, 5),        (12, "male", 47, 15, "yes", 4, 20, 6, 4),        (2, "female", 42, 15, "yes", 2, 17, 6, 3),        (12, "male", 37, 15, "yes", 3, 17, 6, 3),        (12, "male", 37, 15, "yes", 5, 17, 5, 2),        (7, "male", 27, 10, "yes", 2, 20, 6, 4),        (2, "male", 37, 15, "yes", 2, 16, 5, 4),        (12, "female", 32, 15, "yes", 1, 14, 5, 2),        (7, "male", 32, 10, "yes", 3, 17, 6, 3),        (2, "male", 37, 15, "yes", 4, 18, 5, 1),        (7, "female", 27, 1.5, "no", 2, 17, 5, 5),        (3, "female", 47, 15, "yes", 2, 17, 5, 2),        (12, "male", 37, 15, "yes", 2, 17, 5, 4),        (12, "female", 27, 4, "no", 2, 14, 5, 5),        (2, "female", 27, 10, "yes", 4, 14, 1, 5),        (1, "female", 22, 4, "yes", 3, 16, 1, 3),        (12, "male", 52, 7, "no", 4, 16, 5, 5),        (2, "female", 27, 4, "yes", 1, 16, 3, 5),        (7, "female", 37, 15, "yes", 2, 17, 6, 4),        (2, "female", 27, 4, "no", 1, 17, 3, 1),        (12, "female", 17.5, 0.75, "yes", 2, 12, 3, 5),        (7, "female", 32, 15, "yes", 5, 18, 5, 4),        (7, "female", 22, 4, "no", 1, 16, 3, 5),        (2, "male", 32, 4, "yes", 4, 18, 6, 4),        (1, "female", 22, 1.5, "yes", 3, 18, 5, 2),        (3, "female", 42, 15, "yes", 2, 17, 5, 4),        (1, "male", 32, 7, "yes", 4, 16, 4, 4),        (12, "male", 37, 15, "no", 3, 14, 6, 2),        (1, "male", 42, 15, "yes", 3, 16, 6, 3),        (1, "male", 27, 4, "yes", 1, 18, 5, 4),        (2, "male", 37, 15, "yes", 4, 20, 7, 3),        (7, "male", 37, 15, "yes", 3, 20, 6, 4),        (3, "male", 22, 1.5, "no", 2, 12, 3, 3),        (3, "male", 32, 4, "yes", 3, 20, 6, 2),        (2, "male", 32, 15, "yes", 5, 20, 6, 5),        (12, "female", 52, 15, "yes", 1, 18, 5, 5),        (12, "male", 47, 15, "no", 1, 18, 6, 5),        (3, "female", 32, 15, "yes", 4, 16, 4, 4),        (7, "female", 32, 15, "yes", 3, 14, 3, 2),        (7, "female", 27, 7, "yes", 4, 16, 1, 2),        (12, "male", 42, 15, "yes", 3, 18, 6, 2),        (7, "female", 42, 15, "yes", 2, 14, 3, 2),        (12, "male", 27, 7, "yes", 2, 17, 5, 4),        (3, "male", 32, 10, "yes", 4, 14, 4, 3),        (7, "male", 47, 15, "yes", 3, 16, 4, 2),        (1, "male", 22, 1.5, "yes", 1, 12, 2, 5),        (7, "female", 32, 10, "yes", 2, 18, 5, 4),        (2, "male", 32, 10, "yes", 2, 17, 6, 5),        (2, "male", 22, 7, "yes", 3, 18, 6, 2),        (1, "female", 32, 15, "yes", 3, 14, 1, 5))         val data = dataList.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

 

随机森林建模

+ View Code?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

data.createOrReplaceTempView("data")     // 字符类型转换成数值  val labelWhere = "case when affairs=0 then 0 else cast(1 as double) end as label" val genderWhere = "case when gender='female' then 0 else cast(1 as double) end as gender" val childrenWhere = "case when children='no' then 0 else cast(1 as double) end as children"     val dataLabelDF = spark.sql(s"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data")     val featuresArray = Array("gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")     // 字段转换成特征向量  val assembler = new VectorAssembler().setInputCols(featuresArray).setOutputCol("features") val vecDF: DataFrame = assembler.transform(dataLabelDF) vecDF.show(10, truncate = false)     // 将数据分为训练和测试集（30%进行测试） val Array(trainingDF, testDF) = vecDF.randomSplit(Array(0.7, 0.3))     // 索引标签，将元数据添加到标签列中  val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF) //labelIndexer.transform(vecDF).show(10, truncate = false)     // 自动识别分类的特征，并对它们进行索引  // 具有大于5个不同的值的特征被视为连续。  val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(5).fit(vecDF) //featureIndexer.transform(vecDF).show(10, truncate = false)     // 训练随机森林模型 val rf = new RandomForestClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setNumTrees(10)     // 将索引标签转换回原始标签  val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)     // Chain indexers and forest in a Pipeline. val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))     // Train model. This also runs the indexers. val model = pipeline.fit(trainingDF)   // 输出随机森林模型的全部参数值 model.stages(2).extractParamMap()     // 作出预测  val predictions = model.transform(testDF)     // Select example rows to display. predictions.select("predictedLabel", "label", "features").show(10, false)     // 选择（预测标签，实际标签），并计算测试误差 val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) println("Test Error = " + (1.0 - accuracy))     // 这里的stages(2)中的“2”对应pipeline中的“rf”，将model强制转换为RandomForestClassificationModel类型 val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] println("Learned classification forest model:\n" + rfModel.toDebugString)

 

代码执行结果

+ View Code?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760

vecDF.show(10, truncate = false) +-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+ |label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            | +-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+ |0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]| |0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] | |0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]| |0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]| |0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]| |0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] | |0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]| |0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]| |0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]| |0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] | +-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+ only showing top 10 rows     // 将数据分为训练和测试集（30%进行测试） val Array(trainingDF, testDF) = vecDF.randomSplit(Array(0.7, 0.3)) trainingDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double, gender: double ... 8 more fields] testDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double, gender: double ... 8 more fields]     // 索引标签，将元数据添加到标签列中  val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF) labelIndexer: org.apache.spark.ml.feature.StringIndexerModel = strIdx_37df210602df //labelIndexer.transform(vecDF).show(10, truncate = false)     // 自动识别分类的特征，并对它们进行索引  // 具有大于5个不同的值的特征被视为连续。  val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(5).fit(vecDF) featureIndexer: org.apache.spark.ml.feature.VectorIndexerModel = vecIdx_9595c228f520 //featureIndexer.transform(vecDF).show(10, truncate = false)     // 训练随机森林模型 val rf = new RandomForestClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setNumTrees(10) rf: org.apache.spark.ml.classification.RandomForestClassifier = rfc_d0e7623d0b10     // 将索引标签转换回原始标签  val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels) labelConverter: org.apache.spark.ml.feature.IndexToString = idxToStr_32d6938f2c94     // Chain indexers and forest in a Pipeline. val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, rf, labelConverter)) pipeline: org.apache.spark.ml.Pipeline = pipeline_97716da42fed     // Train model. This also runs the indexers. val model = pipeline.fit(trainingDF) model: org.apache.spark.ml.PipelineModel = pipeline_97716da42fed   // 输出随机森林模型的全部参数值 model.stages(2).extractParamMap() res10: org.apache.spark.ml.param.ParamMap = {     rfc_0d830180d598-cacheNodeIds: false,     rfc_0d830180d598-checkpointInterval: 10,     rfc_0d830180d598-featureSubsetStrategy: auto,     rfc_0d830180d598-featuresCol: indexedFeatures,     rfc_0d830180d598-impurity: gini,     rfc_0d830180d598-labelCol: indexedLabel,     rfc_0d830180d598-maxBins: 32,     rfc_0d830180d598-maxDepth: 5,     rfc_0d830180d598-maxMemoryInMB: 256,     rfc_0d830180d598-minInfoGain: 0.0,     rfc_0d830180d598-minInstancesPerNode: 1,     rfc_0d830180d598-predictionCol: prediction,     rfc_0d830180d598-probabilityCol: probability,     rfc_0d830180d598-rawPredictionCol: rawPrediction,     rfc_0d830180d598-seed: 207336481,     rfc_0d830180d598-subsamplingRate: 1.0 }       // 作出预测  val predictions = model.transform(testDF) predictions: org.apache.spark.sql.DataFrame = [label: double, gender: double ... 14 more fields]     predictions.select("predictedLabel", "label", "features").show(10,false) +--------------+-----+-------------------------------------+ |predictedLabel|label|features                             | +--------------+-----+-------------------------------------+ |0.0           |0.0  |[0.0,22.0,0.125,0.0,4.0,12.0,4.0,5.0]| |0.0           |0.0  |[0.0,22.0,0.125,0.0,4.0,14.0,4.0,5.0]| |0.0           |0.0  |[0.0,22.0,0.417,0.0,1.0,17.0,6.0,4.0]| |0.0           |0.0  |[0.0,22.0,0.417,0.0,4.0,14.0,5.0,5.0]| |0.0           |0.0  |[0.0,22.0,0.417,1.0,3.0,14.0,3.0,5.0]| |0.0           |0.0  |[0.0,22.0,0.75,0.0,5.0,18.0,1.0,5.0] | |0.0           |0.0  |[0.0,22.0,1.5,0.0,1.0,14.0,1.0,5.0]  | |0.0           |0.0  |[0.0,22.0,1.5,0.0,4.0,16.0,5.0,3.0]  | |0.0           |0.0  |[0.0,22.0,1.5,0.0,4.0,17.0,5.0,5.0]  | |0.0           |0.0  |[0.0,22.0,1.5,1.0,3.0,12.0,1.0,3.0]  | +--------------+-----+-------------------------------------+ only showing top 10 rows     // 选择（预测标签，实际标签），并计算测试误差 val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy") evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = mcEval_13a195abc422     val accuracy = evaluator.evaluate(predictions) accuracy: Double = 0.7365591397849462     println("Test Error = " + (1.0 - accuracy)) Test Error = 0.26344086021505375     // 这里的stages(2)中的“2”对应pipeline中的“rf”，将model强制转换为RandomForestClassificationModel类型 val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] rfModel: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel (uid=rfc_f7bb5e488533) with 10 trees     println("Learned classification forest model:\n" + rfModel.toDebugString) Learned classification forest model: RandomForestClassificationModel (uid=rfc_f7bb5e488533) with 10 trees   Tree 0 (weight 1.0):     If (feature 2 <= 1.5)      If (feature 5 <= 12.0)       If (feature 6 <= 1.0)        Predict: 0.0       Else (feature 6 > 1.0)        If (feature 2 <= 0.125)         Predict: 0.0        Else (feature 2 > 0.125)         Predict: 1.0      Else (feature 5 > 12.0)       If (feature 0 in {0.0})        If (feature 5 <= 16.0)         Predict: 0.0        Else (feature 5 > 16.0)         If (feature 1 <= 22.0)          Predict: 0.0         Else (feature 1 > 22.0)          Predict: 0.0       Else (feature 0 not in {0.0})        If (feature 2 <= 0.75)         If (feature 4 in {0.0,1.0,2.0,4.0})          Predict: 0.0         Else (feature 4 not in {0.0,1.0,2.0,4.0})          Predict: 0.0        Else (feature 2 > 0.75)         If (feature 1 <= 22.0)          Predict: 0.0         Else (feature 1 > 22.0)          Predict: 1.0     Else (feature 2 > 1.5)      If (feature 1 <= 42.0)       If (feature 1 <= 27.0)        If (feature 5 <= 16.0)         If (feature 6 <= 5.0)          Predict: 0.0         Else (feature 6 > 5.0)          Predict: 1.0        Else (feature 5 > 16.0)         If (feature 4 in {3.0})          Predict: 0.0         Else (feature 4 not in {3.0})          Predict: 0.0       Else (feature 1 > 27.0)        If (feature 4 in {0.0,3.0,4.0})         If (feature 2 <= 4.0)          Predict: 1.0         Else (feature 2 > 4.0)          Predict: 0.0        Else (feature 4 not in {0.0,3.0,4.0})         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 1.0      Else (feature 1 > 42.0)       If (feature 4 in {2.0,4.0})        Predict: 0.0       Else (feature 4 not in {2.0,4.0})        If (feature 4 in {0.0})         Predict: 1.0        Else (feature 4 not in {0.0})         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 0.0   Tree 1 (weight 1.0):     If (feature 7 in {0.0,2.0,4.0})      If (feature 7 in {0.0})       If (feature 1 <= 42.0)        If (feature 4 in {1.0})         Predict: 0.0        Else (feature 4 not in {1.0})         Predict: 1.0       Else (feature 1 > 42.0)        Predict: 0.0      Else (feature 7 not in {0.0})       If (feature 1 <= 17.5)        If (feature 4 in {3.0})         Predict: 0.0        Else (feature 4 not in {3.0})         Predict: 1.0       Else (feature 1 > 17.5)        If (feature 0 in {0.0})         If (feature 4 in {1.0,3.0,4.0})          Predict: 0.0         Else (feature 4 not in {1.0,3.0,4.0})          Predict: 0.0        Else (feature 0 not in {0.0})         If (feature 6 <= 2.0)          Predict: 1.0         Else (feature 6 > 2.0)          Predict: 0.0     Else (feature 7 not in {0.0,2.0,4.0})      If (feature 3 in {0.0})       If (feature 5 <= 14.0)        If (feature 4 in {1.0,3.0})         Predict: 0.0        Else (feature 4 not in {1.0,3.0})         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 1.0       Else (feature 5 > 14.0)        If (feature 0 in {0.0})         Predict: 0.0        Else (feature 0 not in {0.0})         If (feature 4 in {0.0,2.0,3.0,4.0})          Predict: 0.0         Else (feature 4 not in {0.0,2.0,3.0,4.0})          Predict: 1.0      Else (feature 3 not in {0.0})       If (feature 5 <= 12.0)        If (feature 0 in {1.0})         Predict: 0.0        Else (feature 0 not in {1.0})         If (feature 6 <= 1.0)          Predict: 0.0         Else (feature 6 > 1.0)          Predict: 0.0       Else (feature 5 > 12.0)        If (feature 4 in {0.0,2.0,3.0,4.0})         If (feature 1 <= 47.0)          Predict: 0.0         Else (feature 1 > 47.0)          Predict: 1.0        Else (feature 4 not in {0.0,2.0,3.0,4.0})         If (feature 1 <= 22.0)          Predict: 1.0         Else (feature 1 > 22.0)          Predict: 0.0   Tree 2 (weight 1.0):     If (feature 7 in {0.0})      If (feature 4 in {1.0})       Predict: 0.0      Else (feature 4 not in {1.0})       If (feature 6 <= 5.0)        If (feature 1 <= 42.0)         Predict: 1.0        Else (feature 1 > 42.0)         Predict: 0.0       Else (feature 6 > 5.0)        Predict: 0.0     Else (feature 7 not in {0.0})      If (feature 5 <= 16.0)       If (feature 7 in {1.0})        If (feature 6 <= 4.0)         If (feature 2 <= 7.0)          Predict: 0.0         Else (feature 2 > 7.0)          Predict: 1.0        Else (feature 6 > 4.0)         Predict: 1.0       Else (feature 7 not in {1.0})        If (feature 3 in {1.0})         If (feature 1 <= 17.5)          Predict: 1.0         Else (feature 1 > 17.5)          Predict: 0.0        Else (feature 3 not in {1.0})         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 0.0      Else (feature 5 > 16.0)       If (feature 3 in {0.0})        If (feature 4 in {4.0})         Predict: 0.0        Else (feature 4 not in {4.0})         If (feature 5 <= 18.0)          Predict: 0.0         Else (feature 5 > 18.0)          Predict: 0.0       Else (feature 3 not in {0.0})        If (feature 4 in {0.0,3.0,4.0})         If (feature 7 in {2.0})          Predict: 0.0         Else (feature 7 not in {2.0})          Predict: 0.0        Else (feature 4 not in {0.0,3.0,4.0})         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 1.0   Tree 3 (weight 1.0):     If (feature 3 in {0.0})      If (feature 7 in {3.0})       Predict: 0.0      Else (feature 7 not in {3.0})       If (feature 2 <= 10.0)        If (feature 4 in {2.0,3.0,4.0})         If (feature 4 in {4.0})          Predict: 0.0         Else (feature 4 not in {4.0})          Predict: 0.0        Else (feature 4 not in {2.0,3.0,4.0})         If (feature 7 in {0.0,2.0,4.0})          Predict: 0.0         Else (feature 7 not in {0.0,2.0,4.0})          Predict: 1.0       Else (feature 2 > 10.0)        Predict: 1.0     Else (feature 3 not in {0.0})      If (feature 6 <= 2.0)       If (feature 5 <= 16.0)        If (feature 7 in {0.0,1.0,2.0,4.0})         If (feature 4 in {0.0,1.0,3.0,4.0})          Predict: 0.0         Else (feature 4 not in {0.0,1.0,3.0,4.0})          Predict: 1.0        Else (feature 7 not in {0.0,1.0,2.0,4.0})         If (feature 1 <= 22.0)          Predict: 0.0         Else (feature 1 > 22.0)          Predict: 0.0       Else (feature 5 > 16.0)        If (feature 7 in {0.0,1.0,3.0})         Predict: 0.0        Else (feature 7 not in {0.0,1.0,3.0})         Predict: 1.0      Else (feature 6 > 2.0)       If (feature 4 in {0.0,3.0,4.0})        If (feature 7 in {0.0,2.0,3.0,4.0})         If (feature 4 in {3.0,4.0})          Predict: 0.0         Else (feature 4 not in {3.0,4.0})          Predict: 0.0        Else (feature 7 not in {0.0,2.0,3.0,4.0})         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 1.0       Else (feature 4 not in {0.0,3.0,4.0})        If (feature 1 <= 22.0)         If (feature 5 <= 14.0)          Predict: 1.0         Else (feature 5 > 14.0)          Predict: 1.0        Else (feature 1 > 22.0)         If (feature 6 <= 6.0)          Predict: 0.0         Else (feature 6 > 6.0)          Predict: 1.0   Tree 4 (weight 1.0):     If (feature 7 in {0.0,2.0,4.0})      If (feature 7 in {0.0})       If (feature 6 <= 5.0)        If (feature 3 in {0.0})         Predict: 0.0        Else (feature 3 not in {0.0})         If (feature 4 in {2.0,4.0})          Predict: 1.0         Else (feature 4 not in {2.0,4.0})          Predict: 1.0       Else (feature 6 > 5.0)        Predict: 0.0      Else (feature 7 not in {0.0})       If (feature 2 <= 1.5)        If (feature 5 <= 12.0)         If (feature 2 <= 0.125)          Predict: 0.0         Else (feature 2 > 0.125)          Predict: 0.0        Else (feature 5 > 12.0)         If (feature 1 <= 17.5)          Predict: 1.0         Else (feature 1 > 17.5)          Predict: 0.0       Else (feature 2 > 1.5)        If (feature 2 <= 7.0)         If (feature 4 in {1.0,3.0,4.0})          Predict: 0.0         Else (feature 4 not in {1.0,3.0,4.0})          Predict: 0.0        Else (feature 2 > 7.0)         If (feature 5 <= 16.0)          Predict: 0.0         Else (feature 5 > 16.0)          Predict: 0.0     Else (feature 7 not in {0.0,2.0,4.0})      If (feature 5 <= 12.0)       Predict: 0.0      Else (feature 5 > 12.0)       If (feature 4 in {0.0,3.0,4.0})        If (feature 1 <= 47.0)         If (feature 1 <= 22.0)          Predict: 0.0         Else (feature 1 > 22.0)          Predict: 0.0        Else (feature 1 > 47.0)         Predict: 1.0       Else (feature 4 not in {0.0,3.0,4.0})        If (feature 1 <= 27.0)         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 0.0        Else (feature 1 > 27.0)         If (feature 5 <= 14.0)          Predict: 1.0         Else (feature 5 > 14.0)          Predict: 1.0   Tree 5 (weight 1.0):     If (feature 7 in {0.0})      If (feature 1 <= 42.0)       If (feature 6 <= 4.0)        Predict: 1.0       Else (feature 6 > 4.0)        If (feature 4 in {1.0})         Predict: 0.0        Else (feature 4 not in {1.0})         Predict: 1.0      Else (feature 1 > 42.0)       Predict: 0.0     Else (feature 7 not in {0.0})      If (feature 2 <= 1.5)       If (feature 4 in {0.0,2.0,3.0})        If (feature 1 <= 22.0)         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 0.0        Else (feature 1 > 22.0)         Predict: 0.0       Else (feature 4 not in {0.0,2.0,3.0})        If (feature 1 <= 17.5)         If (feature 6 <= 4.0)          Predict: 1.0         Else (feature 6 > 4.0)          Predict: 0.0        Else (feature 1 > 17.5)         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 0.0      Else (feature 2 > 1.5)       If (feature 6 <= 5.0)        If (feature 5 <= 17.0)         If (feature 7 in {2.0,4.0})          Predict: 0.0         Else (feature 7 not in {2.0,4.0})          Predict: 0.0        Else (feature 5 > 17.0)         If (feature 6 <= 1.0)          Predict: 0.0         Else (feature 6 > 1.0)          Predict: 0.0       Else (feature 6 > 5.0)        If (feature 4 in {0.0,3.0,4.0})         If (feature 7 in {3.0,4.0})          Predict: 0.0         Else (feature 7 not in {3.0,4.0})          Predict: 0.0        Else (feature 4 not in {0.0,3.0,4.0})         If (feature 6 <= 6.0)          Predict: 0.0         Else (feature 6 > 6.0)          Predict: 0.0   Tree 6 (weight 1.0):     If (feature 4 in {0.0,3.0,4.0})      If (feature 5 <= 12.0)       If (feature 7 in {1.0,2.0,3.0,4.0})        Predict: 0.0       Else (feature 7 not in {1.0,2.0,3.0,4.0})        If (feature 6 <= 3.0)         Predict: 0.0        Else (feature 6 > 3.0)         Predict: 1.0      Else (feature 5 > 12.0)       If (feature 7 in {0.0,1.0,2.0})        If (feature 6 <= 1.0)         If (feature 7 in {0.0,2.0})          Predict: 0.0         Else (feature 7 not in {0.0,2.0})          Predict: 0.0        Else (feature 6 > 1.0)         If (feature 1 <= 37.0)          Predict: 1.0         Else (feature 1 > 37.0)          Predict: 0.0       Else (feature 7 not in {0.0,1.0,2.0})        If (feature 1 <= 17.5)         If (feature 4 in {3.0})          Predict: 0.0         Else (feature 4 not in {3.0})          Predict: 1.0        Else (feature 1 > 17.5)         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 0.0     Else (feature 4 not in {0.0,3.0,4.0})      If (feature 7 in {0.0,4.0})       If (feature 5 <= 12.0)        If (feature 2 <= 0.125)         Predict: 0.0        Else (feature 2 > 0.125)         If (feature 1 <= 17.5)          Predict: 1.0         Else (feature 1 > 17.5)          Predict: 0.0       Else (feature 5 > 12.0)        If (feature 7 in {0.0})         If (feature 1 <= 42.0)          Predict: 1.0         Else (feature 1 > 42.0)          Predict: 0.0        Else (feature 7 not in {0.0})         If (feature 2 <= 1.5)          Predict: 0.0         Else (feature 2 > 1.5)          Predict: 0.0      Else (feature 7 not in {0.0,4.0})       If (feature 6 <= 4.0)        If (feature 7 in {3.0})         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 0.0        Else (feature 7 not in {3.0})         If (feature 5 <= 16.0)          Predict: 0.0         Else (feature 5 > 16.0)          Predict: 1.0       Else (feature 6 > 4.0)        If (feature 6 <= 6.0)         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 1.0        Else (feature 6 > 6.0)         If (feature 5 <= 18.0)          Predict: 1.0         Else (feature 5 > 18.0)          Predict: 0.0   Tree 7 (weight 1.0):     If (feature 7 in {0.0,2.0,4.0})      If (feature 2 <= 1.5)       If (feature 4 in {1.0,2.0,3.0})        If (feature 1 <= 17.5)         Predict: 1.0        Else (feature 1 > 17.5)         Predict: 0.0       Else (feature 4 not in {1.0,2.0,3.0})        If (feature 5 <= 14.0)         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 1.0        Else (feature 5 > 14.0)         Predict: 0.0      Else (feature 2 > 1.5)       If (feature 7 in {0.0,2.0})        If (feature 4 in {1.0,3.0,4.0})         If (feature 5 <= 16.0)          Predict: 0.0         Else (feature 5 > 16.0)          Predict: 0.0        Else (feature 4 not in {1.0,3.0,4.0})         If (feature 6 <= 5.0)          Predict: 1.0         Else (feature 6 > 5.0)          Predict: 0.0       Else (feature 7 not in {0.0,2.0})        If (feature 4 in {0.0,1.0,3.0})         If (feature 1 <= 42.0)          Predict: 0.0         Else (feature 1 > 42.0)          Predict: 0.0        Else (feature 4 not in {0.0,1.0,3.0})         If (feature 5 <= 16.0)          Predict: 0.0         Else (feature 5 > 16.0)          Predict: 0.0     Else (feature 7 not in {0.0,2.0,4.0})      If (feature 2 <= 0.75)       Predict: 0.0      Else (feature 2 > 0.75)       If (feature 4 in {4.0})        If (feature 6 <= 5.0)         If (feature 1 <= 37.0)          Predict: 1.0         Else (feature 1 > 37.0)          Predict: 0.0        Else (feature 6 > 5.0)         Predict: 0.0       Else (feature 4 not in {4.0})        If (feature 5 <= 12.0)         If (feature 1 <= 27.0)          Predict: 0.0         Else (feature 1 > 27.0)          Predict: 0.0        Else (feature 5 > 12.0)         If (feature 7 in {1.0})          Predict: 1.0         Else (feature 7 not in {1.0})          Predict: 0.0   Tree 8 (weight 1.0):     If (feature 5 <= 16.0)      If (feature 4 in {0.0,1.0})       If (feature 0 in {0.0})        If (feature 2 <= 0.75)         If (feature 1 <= 17.5)          Predict: 1.0         Else (feature 1 > 17.5)          Predict: 0.0        Else (feature 2 > 0.75)         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 0.0       Else (feature 0 not in {0.0})        If (feature 5 <= 12.0)         Predict: 1.0        Else (feature 5 > 12.0)         If (feature 7 in {2.0,4.0})          Predict: 0.0         Else (feature 7 not in {2.0,4.0})          Predict: 0.0      Else (feature 4 not in {0.0,1.0})       If (feature 7 in {0.0,2.0,3.0,4.0})        If (feature 1 <= 22.0)         If (feature 6 <= 3.0)          Predict: 0.0         Else (feature 6 > 3.0)          Predict: 0.0        Else (feature 1 > 22.0)         If (feature 6 <= 6.0)          Predict: 0.0         Else (feature 6 > 6.0)          Predict: 1.0       Else (feature 7 not in {0.0,2.0,3.0,4.0})        If (feature 1 <= 42.0)         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 1.0        Else (feature 1 > 42.0)         Predict: 0.0     Else (feature 5 > 16.0)      If (feature 5 <= 18.0)       If (feature 4 in {3.0})        If (feature 7 in {1.0,2.0,3.0})         Predict: 0.0        Else (feature 7 not in {1.0,2.0,3.0})         If (feature 6 <= 5.0)          Predict: 0.0         Else (feature 6 > 5.0)          Predict: 0.0       Else (feature 4 not in {3.0})        If (feature 2 <= 0.75)         Predict: 0.0        Else (feature 2 > 0.75)         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 1.0      Else (feature 5 > 18.0)       If (feature 1 <= 27.0)        If (feature 7 in {3.0})         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 1.0        Else (feature 7 not in {3.0})         If (feature 2 <= 4.0)          Predict: 0.0         Else (feature 2 > 4.0)          Predict: 1.0       Else (feature 1 > 27.0)        If (feature 6 <= 5.0)         If (feature 6 <= 4.0)          Predict: 0.0         Else (feature 6 > 4.0)          Predict: 0.0        Else (feature 6 > 5.0)         If (feature 4 in {3.0,4.0})          Predict: 0.0         Else (feature 4 not in {3.0,4.0})          Predict: 0.0   Tree 9 (weight 1.0):     If (feature 5 <= 16.0)      If (feature 6 <= 2.0)       If (feature 1 <= 42.0)        If (feature 6 <= 1.0)         If (feature 5 <= 9.0)          Predict: 1.0         Else (feature 5 > 9.0)          Predict: 0.0        Else (feature 6 > 1.0)         If (feature 1 <= 27.0)          Predict: 0.0         Else (feature 1 > 27.0)          Predict: 1.0       Else (feature 1 > 42.0)        Predict: 0.0      Else (feature 6 > 2.0)       If (feature 1 <= 27.0)        If (feature 5 <= 14.0)         If (feature 6 <= 3.0)          Predict: 0.0         Else (feature 6 > 3.0)          Predict: 0.0        Else (feature 5 > 14.0)         Predict: 0.0       Else (feature 1 > 27.0)        If (feature 4 in {1.0,2.0,4.0})         If (feature 5 <= 9.0)          Predict: 0.0         Else (feature 5 > 9.0)          Predict: 0.0        Else (feature 4 not in {1.0,2.0,4.0})         If (feature 7 in {2.0,3.0,4.0})          Predict: 0.0         Else (feature 7 not in {2.0,3.0,4.0})          Predict: 1.0     Else (feature 5 > 16.0)      If (feature 6 <= 4.0)       If (feature 4 in {3.0})        Predict: 0.0       Else (feature 4 not in {3.0})        If (feature 1 <= 42.0)         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 0.0        Else (feature 1 > 42.0)         Predict: 1.0      Else (feature 6 > 4.0)       If (feature 4 in {3.0,4.0})        If (feature 1 <= 37.0)         If (feature 3 in {0.0})          Predict: 0.0         Else (feature 3 not in {0.0})          Predict: 0.0        Else (feature 1 > 37.0)         If (feature 1 <= 42.0)          Predict: 0.0         Else (feature 1 > 42.0)          Predict: 0.0       Else (feature 4 not in {3.0,4.0})        If (feature 4 in {0.0,2.0})         If (feature 7 in {0.0,1.0,2.0})          Predict: 1.0         Else (feature 7 not in {0.0,1.0,2.0})          Predict: 1.0        Else (feature 4 not in {0.0,2.0})         If (feature 0 in {0.0})          Predict: 0.0         Else (feature 0 not in {0.0})          Predict: 0.0

 

随机森林模型调优

+ View Code?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

// 字段转换成特征向量 val assembler = new VectorAssembler().setInputCols(featuresArray).setOutputCol("features") val vecDF: DataFrame = assembler.transform(dataLabelDF) vecDF.show(10, truncate = false)   // 将数据分为训练和测试集（30%进行测试） val Array(trainingDF, testDF) = vecDF.randomSplit(Array(0.7, 0.3))   // 索引标签，将元数据添加到标签列中 val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF) //labelIndexer.transform(vecDF).show(10, truncate = false)   // 自动识别分类的特征，并对它们进行索引 // 具有大于5个不同的值的特征被视为连续。 val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(5).fit(vecDF) //featureIndexer.transform(vecDF).show(10, truncate = false)   // 训练随机森林模型 val rf = new RandomForestClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")   // 将索引标签转换回原始标签 val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)   // Chain indexers and forest in a Pipeline. val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))   // 设置参数网格 //impurity  不纯度 //maxBins  离散化"连续特征"的最大划分数 //maxDepth  树的最大深度 //minInfoGain 一个节点分裂的最小信息增益，值为[0,1] //minInstancesPerNode 每个节点包含的最小样本数 >=1 //numTrees 树的数量 //featureSubsetStrategy // 在每个树节点处分割的特征数，参数值比较多，详细的请参考官方文档 //SubsamplingRate(1.0)  给每棵树分配“学习数据”的比例，范围(0, 1] //maxMemoryInMB  如果太小，则每次迭代将拆分1个节点，其聚合可能超过此大小。 //checkpointInterval  设置检查点间隔（> = 1）或禁用检查点（-1）。 例如 10意味着,每10次迭代,缓存将获得检查点。 //cacheNodeIds  如果为false，则算法将树传递给执行器以将实例与节点匹配。 如果为true，算法将缓存每个实例的节点ID。 缓存可以加速更大深度的树的训练。 用户可以通过设置checkpointInterval来设置检查或禁用缓存的频率。(default = false) //seed 种子 val paramGrid = new ParamGridBuilder()   .addGrid(rf.impurity, Array("entropy", "gini"))   .addGrid(rf.maxBins, Array(32, 64))   .addGrid(rf.maxDepth, Array(5, 7, 10))   .addGrid(rf.minInfoGain, Array(0, 0.5, 1))   .addGrid(rf.minInstancesPerNode, Array(10, 20))   .addGrid(rf.numTrees, Array(20, 50))   .addGrid(rf.featureSubsetStrategy, Array("auto", "sqrt"))   .addGrid(rf.subsamplingRate, Array(0.8, 1))   .addGrid(rf.maxMemoryInMB, Array(256, 512))   .addGrid(rf.checkpointInterval, Array(10, 20))   .addGrid(rf.cacheNodeIds, Array(false, true))   .addGrid(rf.seed, Array(123456L, 111L))   .build()   // 选择（预测标签，实际标签），并计算测试误差。indexedLabel与prediction都是索引化的，因此可以直接比较 val classEvaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy")   // 设置交叉验证 val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(classEvaluator).setEstimatorParamMaps(paramGrid).setNumFolds(5)   // 执行交叉验证，并选择出最好的参数集合 val cvModel = cv.fit(trainingDF)     // 查看全部参数 cvModel.extractParamMap() // cvModel.avgMetrics.length=cvModel.getEstimatorParamMaps.length // cvModel.avgMetrics与cvModel.getEstimatorParamMaps中的元素一一对应 cvModel.avgMetrics.length cvModel.avgMetrics // 参数对应的平均度量   cvModel.getEstimatorParamMaps.length cvModel.getEstimatorParamMaps // 参数组合的集合     cvModel.getEvaluator.extractParamMap() // 评估的参数   cvModel.getEvaluator.isLargerBetter // 评估的度量值是大的好，还是小的好 ,根据评估度量，系统会自动识别 cvModel.getNumFolds // 交叉验证的折数   //################################ // 测试模型 val predictDF: DataFrame = cvModel.transform(testDF).selectExpr(   //"race","poverty","smoke","alcohol","agemth","ybirth","yschool","pc3mth", "features",   "predictedLabel", "label", "features") predictDF.show(20, false)