W3School TIY Editor

import pandas as pd from sklearn import tree from sklearn.tree import DecisionTreeClassifier # 读取数据 df = pd.read_csv("data.csv") # 将分类变量转换为数值 d = {'UK': 0, 'USA': 1, 'N': 2} df['Nationality'] = df['Nationality'].map(d) d = {'YES': 1, 'NO': 0} df['Go'] = df['Go'].map(d) # 定义特征列 features = ['Age', 'Experience', 'Rank', 'Nationality'] # 准备特征矩阵和目标向量 X = df[features] y = df['Go'] # 创建并训练决策树分类器 dtree = DecisionTreeClassifier() dtree = dtree.fit(X, y) # 使用 DataFrame 进行预测（保持特征名称一致） predict_data = pd.DataFrame([[40, 10, 7, 1]], columns=features) prediction = dtree.predict(predict_data) # 输出预测结果和解释 print(prediction) print("[1] means 'GO'") print("[0] means 'NO'")