`autopredictor.bestscore`

Module Contents

Functions

display_best_score(X, scoring_metric)

This function identifies the best score with respect to a specific scoring metric along with the corresponding model.

autopredictor.bestscore.display_best_score(X, scoring_metric)[source]

This function identifies the best score with respect to a specific scoring metric along with the corresponding model. It returns a DataFrame and displays the result in a table format.

Parameters:

X (DataFrame) – A DataFrame containing all scoring metrics results alongside the corresponding model, sorted alphabetically.
scoring_metric (str) – A string containing the regression scoring metric, which is used to display best model.

Returns:

If the scoring metric is found, a dataframe containing the best score and the corresponding model is returned. If the scoring metric is not found, a ValueError is raised.

Return type:

DataFrame

Examples

>>> from autopredictor.bestscore import display_best_score
>>> df = pd.DataFrame({'MAE': [5.6, 3.4],
                              'MSE': [9.4, 21.4],
                              'MAPE': [0.34, 0.45],
                              'R2': [0.239, 0.712]},
                             index=['Linear Regression', 'Random Forest'])
>>> display_best_score(df, 'MAE')
                   MAE
Random Forest  3.4

>>> display_best_score(df, 'F1')
ValueError: Invalid Scoring metric 'F1'.The specified metric is not in the list of available metrics. Available metrics: MAE, MSE, MAPE, R2.

autopredictor.bestscore

Module Contents

Functions

`autopredictor.bestscore`