python金融风控评分卡模型和数据分析_python风控数据查得率

import matplotlib.pyplot as plt

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

df=pd.read_excel(readFileName)

list_columns=list(df.columns[:-1])

x_train,x_test,y_train,y_test=train_test_split(X,y,random_state=0)

#n_estimators表示树的个数，测试中100颗树足够

forest=RandomForestClassifier(n_estimators=trees,random_state=0)

forest.fit(x_train,y_train)

print("random forest with %d trees:"%trees)

print("accuracy on the training subset:{:.3f}".format(forest.score(x_train,y_train)))

print("accuracy on the test subset:{:.3f}".format(forest.score(x_test,y_test)))

print('Feature importances:{}'.format(forest.feature_importances_))

plt.barh(range(n_features),forest.feature_importances_,align='center')

plt.yticks(np.arange(n_features),names)

plt.title("random forest with %d trees:"%trees)

plt.xlabel('Feature Importance')


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决策树可视化

神经网络可视化

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