def evaluate(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
    '\n    Predict with custom classifier model.\n\n    Parameters:\n        estimator: Fitted estimator.\n        X: Input test data.\n        y: Labels for test data.\n\n    Returns:\n        Predicted labels.\n    '
    pred = estimator.predict(X=X)
    report = classification_report(y_true=y, y_pred=pred)
    st.write('Classification Report')
    st.write(report)
    (fpr, tpr, thresholds) = roc_curve(y, pred)
    roc_auc = auc(fpr, tpr)
    (_, _, figure) = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=type(estimator))
    st.pyplot(fig=figure)
Predict with custom classifier model.

Parameters:
    estimator: Fitted estimator.
    X: Input test data.
    y: Labels for test data.

Returns:
    Predicted labels.
def evaluate(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
    '\n    Predict with custom classifier model.\n\n    Parameters:\n        estimator: Fitted estimator.\n        X: Input test data.\n        y: Labels for test data.\n\n    Returns:\n        Predicted labels.\n    '
    pred = estimator.predict(X=X)
    report = classification_report(y_true=y, y_pred=pred)
    st.write('Classification Report')
    st.write(report)
    (fpr, tpr, thresholds) = roc_curve(y, pred)
    roc_auc = auc(fpr, tpr)
    (_, _, figure) = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=type(estimator))
    st.pyplot(fig=figure)