roc_auc= roc_auc_score(y_test, y_pred) 报错信息如下 /Users/wgg/anaconda/lib/python2.7/site-packages/sklearn/metrics/ranking.pycin_binary_clf_curve(y_true, y_score, pos_label, sample_weight)297check_consistent_length(y_true, y_score)298 y_true =column_or_1d(y_true)--> 299 y_sco...
y_pred_proba = model.predict_proba(X_test)[:, 1] # 计算 ROC AUC roc_auc = roc_auc_score(y_test, y_pred_proba) print(f"ROC AUC: {roc_auc}") # 绘制 ROC 曲线 fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba) plt.plot(fpr, tpr, label=f"ROC AUC = {roc_auc:.2f}...
predict(X_test) recall_score(y_test, y_pred) precision_score(y_test, y_pred) precision_score(y_test, y_pred,pos_label=0) recall_score(y_test, y_pred,pos_label=0) roc_auc_score(y_test, y_pred1) print 'roc: ',roc_auc_score(y_test, y_pred1) print 'precision: ',precision_...
y_val.max(axis=1), y_pred.max(axis=1)) else: score = roc_auc_score(self.y_val[:,1], y_pred[:,1]) print("interval evaluation - epoch: {:d} - auc: {:.2f}".format(epoch, score)) if score > self.auc: self.auc = score for f in os.listdir('./'): if f.startswith...
roc_auc_score(y_true, y_pred) y_true = np.full(10, -1, dtype="int") with pytest.raises(ValueError, match=err_msg): roc_auc_score(y_true, y_pred) @pytest.mark.parametrize("curve_func", CURVE_FUNCS) def test_binary_clf_curve_multiclass_error(curve_func): @@ -1332,8 +1328,...
from sklearn.metrics import confusion_matrix, accuracy_score y_pred_class = y_pred_pos > threshold tn, fp, fn, tp = confusion_matrix(y_true, y_pred_class).ravel() accuracy = (tp + tn) / (tp + fp + fn + tn)# or simplyaccuracy_score(y_true, y_pred_class) ...
Scikit-learn新版本发布，一行代码秒升级
precision_score(y_test, y_pred,pos_label=0) recall_score(y_test, y_pred,pos_label=0)roc_auc_score(y_test, y_pred1)print'roc: ',roc_auc_score(y_test, y_pred1)print'precision: ',precision_score(y_test, y_pred)print'recall:', recall_score(y_test, y_pred)print'precision Negati...
auc =roc_auc_score(y, pred) f1 = f1_score(y, pred, average='binary')ifpred.sum() >0else0returnauc, f1 開發者ID:rusty1s,項目名稱:pytorch_geometric,代碼行數:24,代碼來源:signed_gcn.py 示例4: test ▲點讚 6▼ # 需要導入模塊: from sklearn import metrics [as 別名]# 或者: from sk...
因为predict_proba返回的是两列。predict_proba的用法参考这里。 简而言之,你上面的代码改成这样就可以了。 y_pred = clf.predict_proba(X_test)[:, 1] roc_auc= roc_auc_score(y_test, y_pred) 原文:http://sofasofa.io/forum_main_post.php?postid=1001678...