您的y_hats长度仅是测试数据上的长度（20％），因为您在X_test上进行了预测。一旦模型通过验证并且对测试预测满意（通过检查模型在X_test预测上与X_test真实值相比的准确性），您应该在完整数据集（X）上重新运行预测。将这两行添加到底部：
y_hats2 = model.predict(X)df['y_hats'] = y_hats2
*根据您的评论进行 *编辑 ，这是一个更新的结果，返回带有预测的数据集并附加在测试数据集中
from sklearn.datasets import load_irisfrom sklearn.cross_validation import train_test_splitfrom sklearn.tree import DecisionTreeClassifierimport pandas as pdimport numpy as npdata = load_iris()# bear with me for the next few steps... I'm trying to walk you through# how my data object landscape looks... i.e. how I get from raw data # to matrices with the actual data I have, not the iris dataset# put feature matrix into columnar format in dataframedf = pd.Dataframe(data = data.data)# add outcome variabledf_class = pd.Dataframe(data = data.target)# finally, split into train-testX_train, X_test, y_train, y_test = train_test_split(df,df_class, train_size = 0.8)model = DecisionTreeClassifier()model.fit(X_train, y_train)# I've got my predictions nowy_hats = model.predict(X_test)y_test['preds'] = y_hatsdf_out = pd.merge(df,y_test[['preds']],how = 'left',left_index = True, right_index = True)
