he default rule that is true for every observation
        of data set. Without default rule a SlipperBaseEstimator would never
        predict negative
        &#34;&#34;&#34;
        default_rule = []
        features = random.choices(
            range(X.shape[1]),
            k=random.randint(2, 8)
        )

        default_rule.append({
            &#39;feature&#39;: str(features[0]),
            &#39;operator&#39;: &#39;&gt;&#39;,
            &#39;pivot&#39;: str(min(X[:, features[0]]))
        })

 