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How should feature selection and hyperparameter optimization work?
Split your initial dataset into a ”preprocessing dataset” and a ”train/test dataset”. You can do your feature selection + hyperparameter optimization on the ”preprocessing dataset”. Then, you fix your selected features and hyperparameters, and do k-fold cross validation on the ”train/test dataset”.
Which is an example of Recursive feature elimination?
Recursive feature elimination: A recursive feature elimination example showing the relevance of pixels in a digit classification task. Recursive feature elimination with cross-validation: A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation.
How is the robustness of a SVM model built?
Precisely, the robustness of our model is built by combining the strength of three sub-methods; the Recursive Feature Elimination (RFE) for selecting the most useful predictive features, the GridSearchCV for Hyper-Parameters Optimization (HPO) and the Synthetic Minority Oversampling (SMOTE) to overcome the imbalanced data problem.
How is genericunivariateselect used in feature selection?
GenericUnivariateSelect allows to perform univariate feature selection with a configurable strategy. This allows to select the best univariate selection strategy with hyper-parameter search estimator. For instance, we can perform a χ 2 test to the samples to retrieve only the two best features as follows:
Which is the best algorithm for hyperparameter optimization?
— Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms, 2012.
How are hyperparameter values used in machine learning?
This is a minimization function that receives hyperparameter values as input from the search space and returns the loss. This means that during the optimization process, we train the model with selected haypeparameter values and predict the target feature. Then we evaluate the prediction error and give it back to the optimizer.
How does hyperparameter optimization affect a model train?
These parameters are tunable and can directly affect how well a model trains. So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time.