code: def test_sklearn_skewed_classification_overfit():
      """Test sklearn models can overfit 0/1 datasets with few actives."""
      n_samples = 100
      n_features = 3
      n_tasks = 1
  
      # Generate dummy dataset
      np.random.seed(123)
      p = 0.05
      ids = np.arange(n_samples)
      X = np.random.rand(n_samples, n_features)
      y = np.random.binomial(1, p, size=(n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
  
      dataset = dc.data.NumpyDataset(X, y, w, ids)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.roc_auc_score
      )
      sklearn_model = RandomForestClassifier()
      model = dc.models.SklearnModel(sklearn_model)
  
      # Fit trained model
      model.fit(dataset)
      model.save()
  
      # Eval model on train
      scores = model.evaluate(dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.9
  
imports: import os
  import numpy
  import pytest
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  import deepchem
  from deepchem.models.optimizers import Adam
label_desc: Use image node_id 3 for calling a global function with example usage: test_sklearn_skewed_classification_overfit() without return types