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()))
        ```