Multi-classification evaluation indicator Python code

From Sklearn.metrics import Precision_score,recall_score

Print (Precision_score (y_true, y_scores,average= ' micro '))

The Sklearn.metrics module implements some loss, score, and some tool functions to calculate classification performance. Some metrics may require a probability estimate of a positive case, a confidence level, or a binary decision value. Most implementations allow each sample to provide a weighted distribution of the overall score, which is accomplished by the Sample_weight parameter.

Case used by some two classifications (binary classification):

• Matthews_corrcoef (Y_true, y_pred)
• Precision_recall_curve (Y_true, probas_pred)
• Roc_curve (Y_true, y_score[, Pos_label, ...])

Some cases used by multiple classifications (Multiclass):

• Confusion_matrix (Y_true, y_pred[, labels])
• Hinge_loss (Y_true, pred_decision[, labels, ...])

Some multi-label (Multilabel) Cases:

• Accuracy_score (Y_true, y_pred[, normalize, ...])
• Classification_report (Y_true, y_pred[, ...])
• F1_score (Y_true, y_pred[, labels, ...])
• Fbeta_score (Y_true, y_pred, beta[, labels, ...])
• Hamming_loss (Y_true, y_pred[, classes])
• Jaccard_similarity_score (Y_true, y_pred[, ...])
• Log_loss (Y_true, y_pred[, EPS, normalize, ...])
• Precision_recall_fscore_support (Y_true, y_pred)
• Precision_score (Y_true, y_pred[, labels, ...])
• Recall_score (Y_true, y_pred[, labels, ...])
• Zero_one_loss (Y_true, y_pred[, normalize, ...])

There are also some issues that can be used for both two-label and multi-label (not multi-classification):

• Average_precision_score (Y_true, y_score[, ...])
• Roc_auc_score (Y_true, y_score[, average, ...])

Multi-classification evaluation indicator Python code