Kaggle competition: SIIM-ISIC Melanoma Classification
<choose_model_class><EOS> 
import pandas as pd
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<train_model>
import pandas as pd
text_clf.fit(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000],
eval_set=[(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000]), 
(X_train_embeddings['outputs'][5000:,:], train_df.target.values[5000:])],
verbose=200, early_stopping_rounds=20, 
)
<predict_on_test>
df_1 = pd.read_csv('/kaggle/input/model-melanoma-2020/submission.csv')
 df_2a = pd.read_csv('/kaggle/input/model-metadata-melanoma-2020/submission_best_mean.csv')
 df_2b = pd.read_csv('/kaggle/input/model-metadata-melanoma-2020/submission_ensemble_mean.csv')
 df_2c = pd.read_csv('/kaggle/input/model-metadata-melanoma-2020/submission_weighted_ensemble_mean.csv')
 df_3a = pd.read_csv('/kaggle/input/melanoma-effnet-metdata/blended_effnets.csv')
 df_3b = pd.read_csv('/kaggle/input/melanoma-effnet-metdata/ensembled.csv')
 df_4 = pd.read_csv('/kaggle/input/minmax-melanoma-9619/submission.csv')
 df_5 = pd.read_csv('/kaggle/input/melanoma-2020-9648/submission_9648.csv' )
text_clf.fit(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000])
 Y_pred = text_clf.predict(X_train_embeddings['outputs'][5000:] )<compute_train_metric>
df_submission1 = df_1.copy()
df_submission1['target'] = 0.41 * df_1['target'] + 0.04 * df_2c['target'] + 0.55 * df_3b['target']
print(metrics.classification_report(train_df.target[5000:], Y_pred, digits=3),)
 print(metrics.confusion_matrix(train_df.target[5000:], Y_pred))<predict_on_test>
df_submission1.to_csv('submission1.csv', index=False)
 df_submission1
text_clf.fit(X_train_embeddings['outputs'], train_df.target.values)
 pred_test = text_clf.predict(X_test_embeddings['outputs'] )<prepare_output>
df_submission2 = df_1.copy()
df_submission2['target'] = 0.075 * df_1['target'] + 0.1 * df_3b['target'] + 0.375 * df_4['target'] + 0.45 * df_5['target']
df = pd.DataFrame({'cleaned_text' : test_df['text'], 'prediction' : pred_test})
 df.head(20 )<save_to_csv>
df_submission2.to_csv('submission2.csv', index=False)
 df_submission2
sample_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
 sample_submission["target"] = pred_test
sample_submission.to_csv("submission.csv", index=False )<load_from_csv>
df_submission3 = df_1.copy()
df_submission3['target'] = 0.9 * df_1['target'] + 0.1 * df_2b['target']
train = pd.read_csv(".. /input/digit-recognizer/train.csv")
 test = pd.read_csv(".. /input/digit-recognizer/test.csv" )<categorify>
df_submission3.to_csv('submission3.csv', index=False)
 df_submission3
X_train = train.drop(labels=["label"], axis=1)
 Y_train = train['label']
del train
X_train = X_train / 255.
 test = test / 255.
 X_train = X_train.values.reshape(-1, 28, 28, 1)
 test = test.values.reshape(-1, 28, 28, 1)
<split>
df_submission4 = df_1.copy()
df_submission4['target'] = 0.3 * df_1['target'] + 0.1 * df_2b['target'] + 0.3 * df_3b['target'] + 0.3 * df_4['target']
<define_search_space><EOS> 
df_submission4.to_csv('submission4.csv', index=False)
 df_submission4