我想计算不同分类器的交叉验证测试的召回率,精度和f度量. scikit-learn附带了cross_val_score,但遗憾的是这种方法不会返回多个值.
我可以通过调用cross_val_score 三次 来计算这样的度量,但效率不高.有没有更好的解决方案？
到现在为止我写了这个函数:
from sklearn import metrics
def mean_scores(X, y, clf, skf):
cm = np.zeros(len(np.unique(y)) ** 2)
for i, (train, test) in enumerate(skf):
clf.fit(X[train], y[train])
y_pred = clf.predict(X[test])
cm += metrics.confusion_matrix(y[test], y_pred).flatten()
return compute_measures(*cm / skf.n_folds)
def compute_measures(tp, fp, fn, tn):
"""Computes effectiveness measures given a confusion matrix."""
specificity = tn / (tn + fp)
sensitivity = tp / (tp + fn)
fmeasure = 2 * (specificity …