每日总结

机器学习决策树

决策树（decision tree）：是一种基本的分类与回归方法

决策树通常有三个步骤：特征选择、决策树的生成、决策树的修剪。

使用sklearn构建决策树：例子：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd


def datasets_demo():
    iris = load_iris()
    print("鸢尾花数据集\n", iris)
    print("查看数据集描述：\n", iris["DESCR"])
    # print("查看特征值描述：\n", iris.feature_names)
    # print("特征值:\n", iris.data)

    # x_train, x_test, y_train, y_test=train_test_split(iris.data, iris.target, test_size=0.2, random_state=22)
    # print(x_train, x_train.shape)
    return None


def minmax_demo():
    data = pd.read_csv("nsrxx1.csv")
    print(data)

    return None


if __name__ == "__main__":
    minmax_demo()