from xgboost import XGBClassifier
model = XGBClassifier(learning_rate=0.1,max_depth=5,alpha=0.2)
model.fit(x_train,y_train)
p=model.predict_proba(x_test)
m=pd.DataFrame(p)
n=m.iloc[:,1:]
from sklearn.metrics import roc_curve
label=y_test
score=n
fpr,tpr,thresholds= roc_curve(label,score)
ks_value = max(abs(fpr-tpr))

画图，画出曲线

plt.plot(fpr, label='bad')
plt.plot(tpr, label='good')
plt.plot(abs(fpr-tpr), label='diff')

标记ks

x = np.argwhere(abs(fpr-tpr) == ks_value)[0, 0]
plt.plot((x, x), (0, ks_value), label='ks - {:.2f}'.format(ks_value), color='r', marker='o', markerfacecolor='r', markersize=5)
plt.scatter((x, x), (0, ks_value), color='r')
plt.legend()
plt.show()
4.1调参
model = XGBClassifier(max_depth=5,alpha=0.2)
grid={'learning_rate':[0.01, 0.015, 0.025, 0.05, 0.1,0.2]}
gs=GridSearchCV(model,grid,cv=10)
gs.fit(x_train,y_train)

gs.best_score_

gs.best_params_