# select_or_exclude_top_k_features_FP

This feature preprocessor object applies an ANOVA to the training data to find the p-value of all features. It then either uses the top k features with the smallest p-values, or it removes the features with the smallest k p-values. Additionally, this function can be used to remove the top k p-values and then use only the following j next smallest p-values (for example, this can be useful if one is interested in comparing the performance using the most selective 10 neurons to using the next most selective neurons, etc.).

## Properties and Methods

The following properties can be set to control the behavior of this feature preprocessor**(note that either num_features_to_exclude num_features_to_use must be set)**

fp = select_or_exclude_top_k_features_FP

Basic constructor

num_features_to_exclude

This property sets the number of features with the smallest p-values to exclude. This method or set_num_features_to_use (or both) must be set prior to using the object to normalize data.

num_features_to_use

This property sets the number of features with the smallest p-values to use. This property or num_features_to_exclude (or both) must be set prior to using the object to normalize data.

fp = save_extra_info (default = 0)

If this property is set to 1, the p-values for all features will be saved and returned along with the classifier results. It should be noted that setting this value to 1 will greatly increase the size of the saved results file.

The following methods are used by the cross-validator algorithm to apply feature preprocessing to the data:

[fp XTr_preprocesed] = set_properties_with_training_data(fp, XTr, YTr)

Calculates the p-values for each feature by applying an ANOVA to the training data (XTr, YTr). The method then returns the training data with either only the top k most selective neurons, or with the top k features removed (or with the top k features removed and only the next j features used, if both set_num_features_to_use and set_num_features_to_remove have been set).

X_preprocessed = preprocess_test_data(fp, X_data)

Removes or only uses the top k selective neurons (as determined using the training data). This method is usually applied by the cross-validator to the test data.

current_FP_info_to_save = get_current_info_to_save(fp)

Returns the p-values from ANOVAs applied to each feature in the structure current_preprocessing_information_to_save.the_p_values_org_order. These values will be saved the cross-validator algorithm.