Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<train_model>
def ensemble() :
stacked_0 = pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/sample_submission.csv')
 stacked_1 = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionImage.csv')
 stacked_2 = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionTabular.csv')
 stacked_4 = pd.read_csv('/kaggle/input/melanoma-submissions2/submission_multiple_data_source.csv')
 stacked_3 = pd.read_csv('.. /input/csv001/datasets_766461_1344911_submission_rank-then-blend.csv')
 stacked_5 = pd.read_csv('.. /input/csv001/submission0.9504.csv')
 stacked_6 = pd.read_csv('.. /input/csv001/submission_efnet.csv')
 stacked_7 = pd.read_csv('.. /input/csv001/submission_models_blended.csv')
 sub = pd.DataFrame()
sub['image_name'] = stacked_0['image_name']
sub['target'] = np.exp(np.mean(
 [
stacked_1['target'].apply(lambda x: np.log2(x)) , \
stacked_2['target'].apply(lambda x: np.log2(x)) , \
stacked_4['target'].apply(lambda x: np.log2(x)) , \
stacked_3['target'].apply(lambda x: np.log2(x)) ,
stacked_5['target'].apply(lambda x: np.log2(x)) , \
stacked_6['target'].apply(lambda x: np.log2(x)) , \
stacked_7['target'].apply(lambda x: np.log2(x)) ,
], axis=0))
sub.to_csv('submission.csv', index=False, float_format='%.6f' )
gbor = GradientBoostingOtherRegressor(ridge_best, n_estimates=4)
 gbor.fit(X_train_cleaned, y_train)
 analys_model(gbor )<train_on_grid>
ensemble()
<import_modules><EOS> 
stacked_n = pd.read_csv('submission.csv')
 stacked_n.head()