How to use the eli5._feature_weights.get_top_features_filtered function in eli5
To help you get started, we’ve selected a few eli5 examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
TeamHG-Memex / eli5 / eli5 / sklearn / explain_prediction.py View on Github 
def _weights(label_id, scale=1.0):
weights = feature_weights[:, label_id]
return get_top_features_filtered(x, flt_feature_names, flt_indices,
weights, top, scale)def _weights(label_id, scale=1.0):
coef = get_coef(clf, label_id)
scores = _multiply(x, coef)
return get_top_features_filtered(x, flt_feature_names, flt_indices,
scores, top, scale)
return _weightsdef _weights(label_id, scale=1.0):
weights = feature_weights[:, label_id]
return get_top_features_filtered(x, flt_feature_names, flt_indices,
weights, top, scale)eli5
Debug machine learning classifiers and explain their predictions
Package Health Score
55 / 100Popular eli5 functions
- eli5._feature_names.FeatureNames
- eli5._feature_weights.get_top_features_filtered
- eli5.base.Explanation
- eli5.base.FeatureWeight
- eli5.base.FeatureWeights
- eli5.base.TargetExplanation
- eli5.base.WeightedSpans
- eli5.explain.explain_prediction
- eli5.explain.explain_prediction.register
- eli5.explain.explain_weights
- eli5.explain.explain_weights.register
- eli5.explain_prediction
- eli5.explain_weights
- eli5.lime.TextExplainer
- eli5.show_weights
- eli5.sklearn.PermutationImportance
- eli5.sklearn.utils.get_coef
- eli5.transform.transform_feature_names.register
- eli5.utils.argsort_k_largest_positive
- eli5.utils.mask