Model help using Scikit-learn when using GridSearch

by

GridSearchCV as @Gauthier Feuillen said is used to search best parameters of an estimator for given data.
Description of GridSearchCV:-

  1. gcv = GridSearchCV(pipe, clf_params,cv=cv)
  2. gcv.fit(features,labels)
  3. clf_params will be expanded to get all possible combinations separate using ParameterGrid.
  4. features will now be split into features_train and features_test using cv. Same for labels
  5. Now the gridSearch estimator (pipe) will be trained using features_train and labels_inner and scored using features_test and labels_test.
  6. For each possible combination of parameters in step 3, The steps 4 and 5 will be repeated for cv_iterations. The average of score across cv iterations will be calculated, which will be assigned to that parameter combination. This can be accessed using cv_results_ attribute of gridSearch.
  7. For the parameters which give the best score, the internal estimator will be re initialized using those parameters and refit for the whole data supplied into it(features and labels).

Because of last step, you are getting different scores in first and second approach. Because in the first approach, all data is used for training and you are predicting for that data only. Second approach has prediction on previously unseen data.

More Related Contents:

  1. Does GridSearchCV perform cross-validation?
  2. scikit-learn GridSearchCV with multiple repetitions
  3. difference between StratifiedKFold and StratifiedShuffleSplit in sklearn
  4. Using Smote with Gridsearchcv in Scikit-learn
  5. How to extract the decision rules from scikit-learn decision-tree?
  6. scikit-learn & statsmodels – which R-squared is correct?
  7. Is it possible to specify your own distance function using scikit-learn K-Means Clustering?
  8. How to get most informative features for scikit-learn classifiers?
  9. Why is Random Forest with a single tree much better than a Decision Tree classifier?
  10. Feature/Variable importance after a PCA analysis
  11. Cross-validation metrics in scikit-learn for each data split
  12. Recovering features names of explained_variance_ratio_ in PCA with sklearn
  13. scikit-learn cross validation, negative values with mean squared error
  14. LabelEncoder for categorical features?
  15. What is exactly sklearn.pipeline.Pipeline?
  16. Scikit Learn OneHotEncoder fit and transform Error: ValueError: X has different shape than during fitting
  17. Using explicit (predefined) validation set for grid search with sklearn
  18. ValueError: n_splits=10 cannot be greater than the number of members in each class
  19. How to find the importance of the features for a logistic regression model?
  20. TfidfVectorizer in scikit-learn : ValueError: np.nan is an invalid document
  21. scikit-learn random state in splitting dataset
  22. Scikit Learn GridSearchCV without cross validation (unsupervised learning)
  23. How to compute precision, recall, accuracy and f1-score for the multiclass case with scikit learn?
  24. scikit-learn: how to scale back the ‘y’ predicted result
  25. confused about random_state in decision tree of scikit learn
  26. Can I send callbacks to a KerasClassifier?
  27. How to get most informative features for scikit-learn classifier for different class?
  28. Does the SVM in sklearn support incremental (online) learning?
  29. Determining the most contributing features for SVM classifier in sklearn
  30. scikit-learn .predict() default threshold

Leave a Comment