Question

如何通过交叉验证模型获得系数？进行交叉验证时，我会获得CV模型的分数，如何获得系数？

#Split into training and testing
x_train, x_test, y_train, y_test = train_test_split(samples, scores, test_size = 0.30, train_size = 0.70)

clf = svm.SVC(kernel='linear', C=1)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores

我想打印与每个功能相关的系数

   #Print co-efficients of features
    for i in range(0, nFeatures):
    print samples.columns[i],":", coef[0][i]

这个没有交叉验证，它提供系数

#Create SVM model using a linear kernel
model = svm.SVC(kernel='linear', C=C).fit(x_train, y_train)
coef = model.coef_

Answer 1

您可能希望使用model_selection.cross_validate（与return_estimator=True一起使用）而不是cross_val_score。它更加灵活，因此您可以访问用于每个折页的估算器：

from sklearn.svm import SVC
from sklearn.model_selection import cross_validate

clf = SVC(kernel='linear', C=1)
cv_results = cross_validate(clf, x_train, y_train, cv=5, return_estimator=True)

for model in cv_results['estimator']:
    print(model.coef_)

应该给您希望的东西，希望！（您可以通过cv_results['train_score']和cv_results['test_score']访问指标）

如何使用交叉验证模型获取系数

1 个答案: