Contents
Is feature selection a hyperparameter?
Hyperparameter tuning is often useful to increase model performance, while feature selection is undertaken to attain sparse models. We perform hyperparameter tuning and feature selection simultaneously because the choice of features of a model may influence what hyperparameters perform well.
Which strategy is used for tuning Hyperparameters?
Grid search is arguably the most basic hyperparameter tuning method. With this technique, we simply build a model for each possible combination of all of the hyperparameter values provided, evaluating each model, and selecting the architecture which produces the best results.
How does cross validation work in hyperparameter tuning?
The data that is remaining, i.e. everything apart from the test set, is split into K number of folds (subsets). The Cross-Validation then iterates through the folds and at each iteration uses one of the K folds as the validation set while using all remaining folds as the training set.
How long does validation take in XGBoost hyper parameter tuning?
For validation, we used a separate 3.2% of the records (approx. 10,000). Even with this small amount of data, a single round of training and validation takes around 3 seconds, for a given set of hyper-params, which is not negligible considering that we intend to scan the whole hyper-param grid.
How are hyperparameters used in model validation in Python?
What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance. This particular form of cross-validation is a two-fold cross-validation —that is, one in which we have split the data into two sets and used each in turn as a validation set.
What’s the best way to tune a hyperparameter?
One possible way is to manually tune the hyperparameters using educated guesses as starting points, changing some hyperparameters, training the model, evaluating its performance and repeating these steps until we are happy with the performance. That sounds like an unnecessarily tedious approach and it is.