Feature selection is one of the most important tasks a data scientist has when it comes to allocating resources, optimizing, and understanding datasets. In this blog post, we'll take a look at techniques you can use to evaluate feature selection in Python.
Data sets often come filled with features that may not be relevant for the task at hand. Applying feature selection techniques can help identify which features are relevant and which are redundant, making it easier and more efficient to work with.
In this blog post, we'll explore three techniques for selecting features in Python: variance thresholding, recursive feature elimination, and mutual information. We'll also discuss the various metrics to evaluate feature selection results.
Variance thresholding is a basic feature selection technique that eliminates all features that have low variance. Features with variance lower than the defined threshold are removed from the dataset.
In Scikit-learn, a variance threshold filter can be applied using the VarianceThreshold class, which allows you to specify the threshold as a parameter.
from sklearn.feature_selection import VarianceThreshold # set variance threshold threshold = 0.01 # create VarianceThreshold object vt = VarianceThreshold(threshold) # apply filter to data vt_data = vt.fit_transform(data)
Recursive feature elimination (RFE) is a feature selection technique that evaluates feature importance and iteratively removes them, beginning with the least important features.
In Scikit-learn, RFE can be applied using the RFE class. You simply specify the model and pass the data along with the desired number of features as parameters.
from sklearn.feature_selection import RFE from sklearn.ensemble import RandomForestClassifier # create Random Forest object rf = RandomForestClassifier() # set number of features to select n_features = 10 # create RFE object and select features rfe = RFE(rf, n_features) rfe = rfe.fit(X_train, y_train) # apply RFE to data X_train_rfe = rfe.transform(X_train) X_test_rfe = rfe.transform(X_test)
Mutual information is a feature selection technique that looks at the relationship between features and the target variable. It calculates the degree of dependence between the two, and allows you to identify highly relevant features.
In Scikit-learn, Mutual information can be applied using the SelectKBest class. You pass the data along with the desired number of features as parameters.
from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import mutual_info_classif # set number of features to select n_features = 10 # create SelectKBest object selector = SelectKBest(mutual_info_classif, k=n_features) # apply filter to data X_train_mutual_info = selector.fit_transform(X_train, y_train) X_test_mutual_info = selector.transform(X_test)
In order to evaluate the performance of a feature selection techniques, there are three metrics that can be used: accuracy, precision, and recall.
Accuracy measures how closely the model's predictions match the true labels. Precision measures the proportion of true labels among the model’s predictions, while recall measures the proportion of true labels that the model was able to identify.
To calculate these metrics, we can use the sklearn.metrics function.
from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score # calculate metrics accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred)
Feature selection is an important part of the data science workflow. In this blog post, we explored three techniques for selecting features in Python: variance thresholding, recursive feature elimination, and mutual information. We also discussed the various metrics for evaluating feature selection results.