Evalml: SHAP test fails with the Elastic Net Classifier estimator

I think this is a shap issue. I filed https://github.com/slundberg/shap/issues/2000 for discussion. In the short term, I think we can do the following:

• Use link="identity" in the KernelExplainer for linear models
• Use LinearExplainer with logit link, explainer = shap.LinearExplainer(classifier, X, link=shap.links.logit)

A further test that failed:

On main, using alpha=0.5, l1_ratio=0.5, which is what we have prior to this, if we add this test into evalml/tests/model_understanding_tests/prediction_explaination_tests/test_explainers.py:

@pytest.mark.parametrize("estimator", ["Extra Trees Classifier", "Elastic Net Classifier"])
def test_elastic_net(estimator, fraud_100):
    pytest.importorskip('imblearn', reason='Skipping test because imblearn not installed')
    X, y = fraud_100
    pipeline = BinaryClassificationPipeline(component_graph=["Imputer", "One Hot Encoder", "DateTime Featurization Component", estimator])
    pipeline.fit(X=X, y=y)
    pipeline.predict(X)
    importance = explain_predictions(pipeline, X, y, indices_to_explain=[0], top_k_features=4)
    assert report['feature_names'].isnull().sum() == 0
    assert report['feature_values'].isnull().sum() == 0

The test fails:

If we change the alpha and l1_ratio, it still fails.

Ok @bchen1116 , go do it.

Closing with this PR