def METHOD_NAME():
    # test fit and predict
    cl = CleanLearning()
    cl.fit(X, y)
    preds = cl.predict(X)
    cl_r2_score = cl.score(X, y)
    manual_r2_score = r2_score(y, preds)
    assert len(preds) == len(y)
    assert isinstance(cl_r2_score, float)
    assert cl_r2_score == manual_r2_score
    # check if label issues were identified
    label_issues = cl.get_label_issues()
    identified_label_issues = label_issues[label_issues["is_label_issue"] == True].index
    frac_errors_identified = np.mean([e in identified_label_issues for e in error_idx])
    assert frac_errors_identified >= 0.9  # assert most errors were detected
    # compare perf to base LinearRegression model
    cl_score = cl.score(X_test, y_test)
    lr = LinearRegression()
    lr.fit(X, y)
    lr_score = lr.score(X_test, y_test)
    assert cl_score > lr_score
    # test passing in label issues in various forms
    # also test different regression model
    cl = CleanLearning(model=SVR())
    label_issues = cl.find_label_issues(X, y)
    assert isinstance(label_issues, pd.DataFrame)
    cl.fit(X, y, label_issues=label_issues)
    cl.fit(X, pd.Series(y), label_issues=label_issues["is_label_issue"])
    cl.fit(X, list(y), label_issues=label_issues["is_label_issue"].values)