def _create_model()

in python/pipelines/components/python/component.py [0:0]


    def _create_model(params):
        model = Pipeline([
            ('transform', ColumnTransformer(
                transformers=[
                    ('tfidf',
                    TfidfTransformer(norm='l2'),
                    list(range(columns_to_skip, len(features) + columns_to_skip))  # Skipping the first n columns
                    )
                ]
            )),
            ('model', KMeans(
                init='k-means++', n_init='auto',
                random_state=42,
                **params)
            )
        ])

        return model