balanced_classification_sampler¶
Module Contents¶
Classes Summary¶
Class for balanced classification downsampler. |
Contents¶
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class
evalml.preprocessing.data_splitters.balanced_classification_sampler.BalancedClassificationSampler(sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0)[source]¶ Class for balanced classification downsampler.
- Parameters
sampling_ratio (float) – The smallest minority:majority ratio that is accepted as ‘balanced’. For instance, a 1:4 ratio would be represented as 0.25, while a 1:1 ratio is 1.0. Must be between 0 and 1, inclusive. Defaults to 0.25.
sampling_ratio_dict (dict) – A dictionary specifying the desired balanced ratio for each target value. Overrides sampling_ratio if provided. Defaults to None.
min_samples (int) – The minimum number of samples that we must have for any class, pre or post sampling. If a class must be downsampled, it will not be downsampled past this value. To determine severe imbalance, the minority class must occur less often than this and must have a class ratio below min_percentage. Must be greater than 0. Defaults to 100.
min_percentage (float) – The minimum percentage of the minimum class to total dataset that we tolerate, as long as it is above min_samples. To determine severe imbalance, the minority class must have a class ratio below this and must occur less often than min_samples. Must be between 0 and 0.5, inclusive. Defaults to 0.1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.
Methods
Resampling technique for this sampler.