然后导入必要的库：
import numpy as np import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsimport warningswarnings.filterwarnings('ignore')%matplotlib inline
载入数据：
train_df=pd.read_csv("train.csv")train_df.head()
输出如下：
以我最喜欢的随机森林算法为例：
from sklearn.ensemble import RandomForestClassifiermodel = RandomForestClassifier(criterion='gini', n_estimators=700, min_samples_split=10,min_samples_leaf=1, max_features='auto',oob_score=True, random_state=1,n_jobs=-1)model.fit(X_train,y_train)prediction_rm=model.predict(X_test)print('--------------The Accuracy of the model----------------------------')print('The accuracy of the Random Forest Classifier is', round(accuracy_score(prediction_rm,y_test)*100,2))kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal partsresult_rm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')print('The cross validated score for Random Forest Classifier is:',round(result_rm.mean()*100,2))y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap="summer")plt.title('Confusion_matrix', y=1.05, size=15)
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哇哦！准确率高达 83%。就第一次尝试而言，这个结果已经很好了。
交叉验证分数的意思是 K 折验证方法。如果 K=10，就是说要把数据分成 10 个变量，计算所有分数的均值，并将它们作为最终分数。