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This method provide a similar functionality like the one we get with BinningProcess.transform(), but return the score contribution for each feature. Here is the link to the idea #345 (comment) Here is an example of usage. ```python import pandas as pd from optbinning import Scorecard, BinningProcess from sklearn.datasets import load_breast_cancer data = load_breast_cancer() X_train = pd.DataFrame( data=data.data, columns=data.feature_names ) y_train = data.target model = Scorecard( binning_process=BinningProcess(variable_names=data.feature_names), estimator=LogisticRegression(random_state=34), rounding=True, scaling_method="min_max", scaling_method_params={"min": 0, "max": 250} ) model.fit(X_train, y_train) print(model.transform(X_train.head())) ```
Added method to provide .transform to the Scorecard Class
…ameters when sample_weight provided
Return expected number of values in compute_prebins for ContinuousOptimalBinning
Fix std in the summary statistics
Use weighted min_bin_size and max_bin_size when sample_weight provided
guillermo-navas-palencia
added
bug
…csv to boston_housing.csv
Move Boston Dataset from External URL to Repository
…ce_all_finite and update scikit-learn to version 1.6.0
…nsure-all-finite Replace deprecated force_all_finite parameter and update scikit-learn version requirement
feat: add to_dict method for binning table serialization
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Updates for version 0.21
transformmethod in scorecard Added method to provide .transform to the Scorecard Class #346.to_dictmethod for binning table serialization feat: add to_dict method for binning table serialization #371.