sklearn 的 cross_val_score()
Python 中的模型验证
Kasey Jones
Data Scientist
cross_val_score()
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
estimator：要使用的模型
X：特征数据集
y：目标数组
cv：交叉验证折数
cross_val_score(estimator=rfc, X=X, y=y, cv=5)
使用 scoring 和 make_scorer
cross_val_score 的 scoring 参数：
# Load the Methods
from sklearn.metrics import mean_absolute_error, make_scorer
# Create a scorer
mae_scorer = make_scorer(mean_absolute_error)
# Use the scorer
cross_val_score(<estimator>, <X>, <y>, cv=5, scoring=mae_scorer)
加载所有 sklearn 方法
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error, make_scorer
创建模型和评分器
rfc = RandomForestRegressor(n_estimators=20, max_depth=5, random_state=1111)
mse = make_scorer(mean_squared_error)
运行 cross_val_score()
cv_results = cross_val_score(rfc, X, y, cv=5, scoring=mse)
访问结果
print(cv_results)
[196.765, 108.563, 85.963, 222.594, 140.942]
报告均值与标准差：
print('The mean: {}'.format(cv_results.mean()))
print('The std: {}'.format(cv_results.std()))
The mean: 150.965
The std: 51.676
让我们来练习！
Python 中的模型验证
