piml.scored_test.test_weakspot

piml.scored_test.test_weakspot(x, y, prediction, prediction_proba=None, feature_names=None, feature_types=None, target_name=None, train_idx=None, test_idx=None, task_type=None, random_state=None, slice_method=None, slice_features=None, metric=None, bins=None, threshold=None, min_samples=None, use_test=None, figsize=None)

Get marginal weakspot result based on a given feature.

Parameters:

xndarray of shape (n_samples, n_features), default=None

The covariate data.

yndarray of shape (n_samples, ), default=None

The actual response.

predicitonndarray of shape (n_samples, ), default=None

The model prediction for regression tasks and predicted class for binary classification tasks.

prediciton_probandarray of shape (n_samples, ), default=None

The predicted probability of the positive class in binary classificaiton tasks. Not need for regression tasks.

task_type{‘regression’, ‘classification’}, default=None

The task type.

feature_nameslist, default=None

Feature names.

feature_types: list, default=None

Feature types, can be ‘numerical’ or ‘categorical’.

target_namestr, default=None

Target name.

train_idxarray-like of shape (n_samples_train,), default=None

If train_idx and test_idx are not None, it will be ignored.

test_idxarray-like of shape (n_samples_test,), default=None

If train_idx and test_idx are not None, it will be ignored.

random_stateint, default=None

Random seed for train / test split. If None, it will be 0.

slice_featureslist, default=None

List of slicing features (at most 2) for Weakspot test.

slice_method{‘histogram’, ‘tree’}, default=None

The slicing method for WeakSpot and Overfit tests. If None, it will be ‘histogram’.

• ‘histogram’: default, use equal-space binning;

• ‘tree’: fit a decision tree to generate regions.

metric{‘MSE’, ‘MAE’, ‘R2’, ‘ACC’, ‘AUC’, ‘F1’, ‘LogLoss’, ‘Brier’}, default=None

Performance metric.

• For classification tasks: ‘ACC’, ‘AUC’, ‘F1’, ‘LogLoss’, ‘Brier’.

• For regression tasks: ‘MSE’, ‘MAE’, ‘R2’.

binsint, default=None

The number of bins. If None, it will be 10.

thresholdfloat, default=None

The minimal error gap for an overfit region. If None, it will be 1.1.

min_samplesint, default=None

The minimal sample size for selected regions. If None, it will be 20.

use_testbool, default=None

Whether to use test data or not. If None, it will be False.

figsizetuple, default=None

Figure size of the plot. If None, it will be (8, 6).