     penalty=&#39;l1&#39;,
                                   prediction_task=&#39;regression&#39;,
                                   max_rules=30,
                                   cv=True,
                                   random_state=None) -&gt; float:
    coef_zero_threshold = 1e-6 / np.mean(np.abs(y))
    alpha_scores = []

    if prediction_task == &#39;regression&#39;:
        alphas = _alpha_grid(X, y)
    elif prediction_task == &#39;classification&#39;:
        # LogisticRegression accepts inv