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@ -344,8 +344,8 @@ pipeline.fit(X_train,y_train)
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pred = pipeline.predict(X_test)
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pred = pipeline.predict(X_test)
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# calculate RMSE and determination
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# calculate RMSE and determination
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rmse = np.sqrt(mean_squared_error(y_test,pred))
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rmse = mean_squared_error(y_test, pred, squared=False)
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print(f'RMSE: {rmse:3.3} ({rmse/np.mean(pred)*100:3.3}%)')
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print(f'RMSE: {rmse:3.3} ({rmse/pred.mean()*100:3.3}%)')
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score = pipeline.score(X_train,y_train)
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score = pipeline.score(X_train,y_train)
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print('Model determination: ', score)
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print('Model determination: ', score)
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