Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<predict_on_test>
import numpy as np
import pandas as pd
predictions = model.predict(X_test)
 predictions = np.argmax(predictions, axis = 1)
 predictions<save_to_csv>
sub_image = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionImage.csv')
 sub_tabular_70 = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionTabular.csv')
 sub_multiple = pd.read_csv('/kaggle/input/melanoma-submissions2/submission_multiple_data_source.csv')
 sub_public_merge = pd.read_csv('/kaggle/input/submission-9/submission_935.csv')
 sub = pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/sample_submission.csv')
 sub_tabular_78 = pd.read_csv('/kaggle/input/tabular-prediction/submission_tabular_78.csv')
 sub_mean = pd.read_csv('/kaggle/input/siim-isic-multiple-model-training-stacking-923/submission_mean.csv' )
result=pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)) ,"Label": predictions})
 result.to_csv("mnist_cnn_only_v1.csv", index=False, header=True )<load_from_csv>
sub.target = sub_mean.target *0.29 + sub_public_merge.target *0.69 + sub_tabular_78.target *0.02
<count_missing_values><EOS> 
sub.to_csv('submission.csv', index = False )