Custom Scoring Metric
elnurmdov
Registered Posts: 5 ✭✭✭
I want to create custom scoring metric to measure precision at first 100 predictions (100 highest probability), but as I understand from the comment in score function, I should pass needs_proba = True in order to get 2 dimensional y_pred to get probabilities and use it to sort my predictions. I don't know where I can pass this parameter
def score(y_valid, y_pred): """ Custom scoring function. Must return a float quantifying the estimator prediction quality. - y_valid is a pandas Series - y_pred is a numpy ndarray with shape: - (nb_records,) for regression problems and classification problems where 'needs probas' (see below) is false (for classification, the values are the numeric class indexes) - (nb_records, nb_classes) for classification problems where 'needs probas' is true - [optional] X_valid is a dataframe with shape (nb_records, nb_input_features) - [optional] sample_weight is a numpy ndarray with shape (nb_records,) NB: this option requires a variable set as "Sample weights" """
Operating system used: MacOS