CODEWITHSUNDEEP
pythonscikit-learnsvmcross-validationfeature-selection
Y. Gopee
Y. Gopee

Reputation: 113

Recursive Feature Elimination and Grid Search for SVR using scikit-learn

I'm using SVR to solve a prediction problem and I would like to do feature selection as well as hyper-parameters search. I'm trying to use both RFECV and GridSearchCV but I'm receiving errors from my code.

My code is as follow:

def svr_model(X, Y):
estimator=SVR(kernel='rbf')
param_grid={
    'C': [0.1, 1, 100, 1000],
    'epsilon': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10],
    'gamma': [0.0001, 0.001, 0.005, 0.1, 1, 3, 5]
}

selector = RFECV(estimator, step = 1, cv = 5)

gsc = GridSearchCV(
    selector,
    param_grid,
    cv=5, scoring='neg_root_mean_squared_error', verbose=0, n_jobs=-1)

grid_result = gsc.fit(X, Y)


best_params = grid_result.best_params_

best_svr = SVR(kernel='rbf', C=best_params["C"], epsilon=best_params["epsilon"], gamma=best_params["gamma"],
               coef0=0.1, shrinking=True,
               tol=0.001, cache_size=200, verbose=False, max_iter=-1)

scoring = {
           'abs_error': 'neg_mean_absolute_error',
           'squared_error': 'neg_mean_squared_error',
           'r2':'r2'}

scores = cross_validate(best_svr, X, Y, cv=10, scoring=scoring, return_train_score=True, return_estimator = True)
return scores

The errors is

ValueError: Invalid parameter C for estimator RFECV(cv=5,
  estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
                gamma='scale', kernel='rbf', max_iter=-1, shrinking=True,
                tol=0.001, verbose=False),
  min_features_to_select=1, n_jobs=None, scoring=None, step=1, verbose=0). Check the list of available parameters with `estimator.get_params().keys()`.

I'm quite new to using machine learning, any help would be highly appreciated.

Upvotes: 0

Views: 1838

Answers (1)

mujjiga
mujjiga

Reputation: 16876

Grid search runs the selector initialized with different combinations of parameters passed in the param_grid. But in this case, we want the grid search to initialize the estimator inside the selector. This is achieved by using the dictionary naming style <estimator>__<parameter>. Follow the docs for more details.

Working code

estimator=SVR(kernel='linear')
selector = RFECV(estimator, step = 1, cv = 5)

gsc = GridSearchCV(
    selector,
    param_grid={
        'estimator__C': [0.1, 1, 100, 1000],
        'estimator__epsilon': [0.0001, 0.0005],
        'estimator__gamma': [0.0001, 0.001]},

    cv=5, scoring='neg_mean_squared_error', verbose=0, n_jobs=-1)

grid_result = gsc.fit(X, Y)

Two other bugs in your code

  1. neg_root_mean_squared_error is not a valid scoring method
  2. rbf kernel does not return feature importance, hence you cannot use this kernel if you want to use RFECV

Upvotes: 2

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