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一.学习背景

        原文来自scikit-learn的学习拓展，根据樱花分类示例衍生而来。源文开源地址：scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation，想学机器学习和数据挖掘的可以去瞧瞧！

二.读取csv文档

df = pd.read_csv('./test_data.csv')
print("df: ", df)
# 假设目标变量是列名为 'target' 的列
#X = df.drop('target', axis=1)  # 特征axis:轴的方向，0为行，1为列，默认为0
X = df.drop(columns=['z'], inplace=False)
y = df['z']  # 目标变量

三.划分数据集

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_test2=[[2,10],[0,3],[3,3],[3,7],[0,6],[1,6],[1,3],[1,4]]
print("X_test: ", X_test)
print("y_test: ", y_test)

四. 创建决策树分类器 

clf = DecisionTreeClassifier(random_state=42)

 五.训练模型

clf.fit(X_train, y_train)

六.预测测试集

y_pred = clf.predict(X_test2)
print("y_pred: ", y_pred)

七.源码 

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, accuracy_score
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris# 读取CSV文件
df = pd.read_csv('./test_data.csv')
print("df: ", df)
# 假设目标变量是列名为 'target' 的列
#X = df.drop('target', axis=1)  # 特征axis:轴的方向，0为行，1为列，默认为0
X = df.drop(columns=['z'], inplace=False)
y = df['z']  # 目标变量# iris = load_iris()
# X, y = iris.data, iris.target
# print("X: ", X)
# print("y: ", y)
# df = pd.DataFrame(X, columns=iris.feature_names)
# df['target'] = y
#print("df: ", df)# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_test2=[[2,10],[0,3],[3,3],[3,7],[0,6],[1,6],[1,3],[1,4]]
print("X_test: ", X_test)
print("y_test: ", y_test)# 创建决策树分类器
clf = DecisionTreeClassifier(random_state=42)# 训练模型
clf.fit(X_train, y_train)# 预测测试集
y_pred = clf.predict(X_test2)
print("y_pred: ", y_pred)
#

八.推测演示结果

        源数据csv文档

 

根据数据x,y预测z的结果，预测[2,10],[0,3],[3,3],[3,7],[0,6],[1,6],[1,3],[1,4]结果，下面是推演过程。