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  1. # some init code omitted
  2. X_train, X_test, y_train, y_test = train_test_split(X, y)
  3.    
  4. tuned_params = [{'solver': ['sgd'], 'learning_rate': ['constant'], "learning_rate_init" : [0.001, 0.01, 0.05, 0.1]},
  5.                 {"learning_rate_init" : [0.001, 0.01, 0.05, 0.1]}]
  6.    
  7. cv_method = KFold(n_splits=4, shuffle=True)
  8. model = MLPClassifier()
  9.    
  10. grid = GridSearchCV(estimator=model, param_grid=tuned_params, cv=cv_method, scoring='accuracy')
  11. grid.fit(X_train, y_train)
  12. y_pred = grid.predict(X_test)
  13.    
  14. print(grid.best_score_)
  15. print(accuracy_score(y_test, y_pred))
  16.    
  17. A dict with keys as column headers and values as columns,
  18. that can be imported into a pandas DataFrame
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