Multi-classification evaluation indicator Python code

Source: Internet
Author: User

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

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