在业务中，我们经常需要对数据建模并预测。简单的情况下，我们采用 if else 判断（一棵树）即可。但如果预测结果与众多因素有关，而每一个特征的权重又不尽相同。
所以我们如何把这些特征的权重合理的找出来,xgboost正是这样一种算法。
xgboost的原理大致是会构建多棵决策树，来提高预测率。原谅我渣渣的数学，资料很多：（https://www.jianshu.com/p/7467e616f227）
这里记录下python demo
参考网址：https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/
经验：在模型训练中，参数的调整固然重要，但特征的辨识度更加重要，所以加入的特征辨识度一定要高，这样训练出的模型准确率才能高。
# First XGBoost model for Pima Indians dataset from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] Y = dataset[:,8] # split data into train and test sets seed = 7 test_size = 0.33 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed) # fit model no training data model = XGBClassifier() model.fit(X_train, y_train) # make predictions for test data y_pred = model.predict(X_test) predictions = [round(value) for value in y_pred] # evaluate predictions accuracy = accuracy_score(y_test, predictions) print("Accuracy: %.2f%%" % (accuracy * 100.0))
