0ef66bff7843bc7f9c4ff956c90f3a5bf404d08a,examples/knn_example.py,,,#,7

Before Change

    y_test_pred = clf.predict(X_test)
    y_test_score = clf.decision_function(X_test)

    print("Precision@n on train data is",
          precision_n_scores(y_train, y_train_score))
    print("ROC on train data is", roc_auc_score(y_train, y_train_score))

    print("Precision@n on test data is",
          precision_n_scores(y_test, y_test_score))

After Change

    ax = fig.add_subplot(223)
    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train_pred)
    plt.title("Train prediction by kNN")
    legend_elements = [Line2D([0], [0], marker="o", color="w", label="normal",
                              markerfacecolor="0", markersize=8),
                       Line2D([0], [0], marker="o", color="w", label="outlier",
                              markerfacecolor="yellow", markersize=8)]

Instances