toad.metrics module

toad.metrics.AIC(y_pred, y, k, lf=None)

Akaike Information Criterion

Parameters:

• y_pred (array-like) –
• y (array-like) –
• k (int) – number of featuers
• lf (float) – result of likelihood function

toad.metrics.AUC(score, target)

AUC Score

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target

Returns:

auc score

Return type:

float

toad.metrics.BIC(y_pred, y, k, lf=None)

Bayesian Information Criterion

Parameters:

• y_pred (array-like) –
• y (array-like) –
• k (int) – number of featuers
• lf (float) – result of likelihood function

toad.metrics.F1(score, target, split='best', return_split=False)

calculate f1 value

Parameters:

• score (array-like) –
• target (array-like) –

Returns:

best f1 score float: best spliter

Return type:

float

toad.metrics.KS(score, target)

calculate ks value

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target

Returns:

the max KS value

Return type:

float

toad.metrics.KS_bucket(score, target, bucket=10, method='quantile', **kwargs)

calculate ks value by bucket

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target
• bucket (int) – n groups that will bin into
• method (str) – method to bin score. quantile (default), step

Returns:

DataFrame

toad.metrics.KS_by_col(df, by='feature', score='score', target='target')

toad.metrics.MSE(y_pred, y)

mean of squares due to error

toad.metrics.PSI(test, base, combiner=None, return_frame=False)

calculate PSI

Parameters:

• test (array-like) – data to test PSI
• base (array-like) – base data for calculate PSI
• combiner (Combiner|list|dict) – combiner to combine data
• return_frame (bool) – if need to return frame of proportion

Returns:

float|Series

toad.metrics.SSE(y_pred, y)

sum of squares due to error