Contents
Why do we use GridSearchCV?
GridSearchCV is a library function that is a member of sklearn’s model_selection package. It helps to loop through predefined hyperparameters and fit your estimator (model) on your training set. In addition to that, you can specify the number of times for the cross-validation for each set of hyperparameters.
What does GridSearchCV return?
Returns the score on the given data, if the estimator has been refit. This uses the score defined by scoring where provided, and the best_estimator_. score method otherwise. Input data, where n_samples is the number of samples and n_features is the number of features.
What is RandomizedSearchCV?
RandomizedSearchCV implements a fit and a score method. It also implements predict, predict_proba, decision_function, transform and inverse_transform if they are implemented in the estimator used. If at least one parameter is given as a distribution, sampling with replacement is used.
What is the difference between GridSearchCV and RandomizedSearchCV?
We have discussed both the approaches to do the tuning that is GridSearchCV and RandomizedSeachCV. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly.
What is Hypertuning?
A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem.
What is random search algorithm?
A random search algorithm refers to an algorithm that incorporates some kind of randomness or probability (typically in the form of a pseudorandom number generator) in its methodology. Randomness may also be included when the objective function is only available through noisy measurements.
What is cross validation test?
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
Why is cross validation needed?
Cross Validation is a very useful technique for assessing the effectiveness of your model, particularly in cases where you need to mitigate overfitting. It is also of use in determining the hyper parameters of your model, in the sense that which parameters will result in lowest test error.