You will find the optimal regression tree for the dataset heart disease_train.csv and return its prediction...

 

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You will find the optimal regression tree for the dataset heart disease_train.csv and return its prediction to heart disease_test.csv. You will be evaluated based on square_loss. You will get a full score if the test loss on your classifier is less than 0.17. You may use any functions that you implemented in the previous project. def square_loss(pred, truth): return np.mean((pred - truth)**2) def test(): prediction: the prediction of your classifier on the heart disease_test.csv prediction = None Xtrain, ytrain = load_data(file='heart disease_train.csv', label=True) ytrain-ytrain>0 Xtest = load_data(file='heart disease_test.csv', label=False) # YOUR CODE HERE raise NotImplementedError() return prediction You will find the optimal regression tree for the dataset heart disease_train.csv and return its prediction to heart disease_test.csv. You will be evaluated based on square_loss. You will get a full score if the test loss on your classifier is less than 0.17. You may use any functions that you implemented in the previous project. def square_loss(pred, truth): return np.mean((pred - truth)**2) def test(): prediction: the prediction of your classifier on the heart disease_test.csv prediction = None Xtrain, ytrain = load_data(file='heart disease_train.csv', label=True) ytrain-ytrain>0 Xtest = load_data(file='heart disease_test.csv', label=False) # YOUR CODE HERE raise NotImplementedError() return prediction

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