CODEWITHSUNDEEP
pythonmachine-learningscikit-learnrandom-forestgrid-search
Rookie_123
Rookie_123

Reputation: 2007

Fitting sklearn GridSearchCV model

I am trying to solve a regression problem on Boston Dataset with help of random forest regressor.I was using GridSearchCV for selection of best hyperparameters.

Problem 1

Should I fit the GridSearchCV on some X_train, y_train and then get the best parameters.

OR

Should I fit it on X, y to get best parameters.(X, y = entire dataset)

Problem 2

Say If I fit it on X, y and get the best parameters and then build a new model on these best parameters. Now how should I train this new model on ?

Should I train the new model on X_train, y_train or X, y.

Problem 3

If I train new model on X,y then how will I validate the results ?

My code so far

   #Dataframes
    feature_cols = ['CRIM','ZN','INDUS','NOX','RM','AGE','DIS','TAX','PTRATIO','B','LSTAT']

    X = boston_data[feature_cols]
    y = boston_data['PRICE']

Train Test Split of Data

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1)

Grid Search to get best hyperparameters

from sklearn.grid_search import GridSearchCV
param_grid = { 
    'n_estimators': [100, 500, 1000, 1500],
    'max_depth' : [4,5,6,7,8,9,10]
}

CV_rfc = GridSearchCV(estimator=RFReg, param_grid=param_grid, cv= 10)
CV_rfc.fit(X_train, y_train)

CV_rfc.best_params_ 
#{'max_depth': 10, 'n_estimators': 100}

Train a Model on the max_depth: 10, n_estimators: 100

RFReg = RandomForestRegressor(max_depth = 10, n_estimators = 100, random_state = 1)
RFReg.fit(X_train, y_train)
y_pred = RFReg.predict(X_test)
y_pred_train = RFReg.predict(X_train)

RMSE: 2.8139766730629394

I just want some guidance with what the correct steps would be

Upvotes: 7

Views: 5804

Answers (2)

Matthieu Brucher
Matthieu Brucher

Reputation: 22023

This is a valid concern indeed.

Problem 1

The GridSearchCV does cross validation indeed to find the proper set of hyperparameters. But you should still have a validation set to make sure that the optimal set of parameters is sound for it (so that gives in the end train, test, validation sets).

Problem 2

The GridSearchCV already gives you the best estimator, you don't need to train a new one. But actually CV is just to check if the building is sound, you can train then on the full dataset (see https://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation for a full detailed discussion).

Problem 3

What you already validated is the way you trained your model (i.e. you already validated that the hyperparameters you found are sound and the training works as expected for the data you have).

Upvotes: 3

FMarazzi
FMarazzi

Reputation: 623

In general, to tune the hyperparameters, you should always train your model over X_train, and use X_test to check the results. You have to tune the parameters based on the results obtained by X_test.

You should never tune hyperparameters over the whole dataset because it would defeat the purpose of the test/train split (as you correctly ask in the Problem 3).

Upvotes: 3

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