sklift.metrics.average_squared_deviation¶
- sklift.metrics.metrics.average_squared_deviation(y_true_train, uplift_train, treatment_train, y_true_val, uplift_val, treatment_val, strategy='overall', bins=10)[source]¶
Compute the average squared deviation.
The average squared deviation (ASD) is a model stability metric that shows how much the model overfits the training data. Larger values of ASD mean greater overfit.
- Parameters
y_true_train (1d array-like) – Correct (true) target values for training set.
uplift_train (1d array-like) – Predicted uplift for training set, as returned by a model.
treatment_train (1d array-like) – Treatment labels for training set.
y_true_val (1d array-like) – Correct (true) target values for validation set.
uplift_val (1d array-like) – Predicted uplift for validation set, as returned by a model.
treatment_val (1d array-like) – Treatment labels for validation set.
strategy (string, ['overall', 'by_group']) –
Determines the calculating strategy. Default is ‘overall’.
'overall'
:The first step is taking the first k observations of all test data ordered by uplift prediction (overall both groups - control and treatment) and conversions in treatment and control groups calculated only on them. Then the difference between these conversions is calculated.
'by_group'
:Separately calculates conversions in top k observations in each group (control and treatment) sorted by uplift predictions. Then the difference between these conversions is calculated
bins (int) – Determines the number of bins (and the relative percentile) in the data. Default is 10.
- Returns
average squared deviation
- Return type
float
References
René Michel, Igor Schnakenburg, Tobias von Martens. Targeting Uplift. An Introduction to Net Scores.