Can you name three feature selection techniques in Machine Learning?

Certainly! Three common feature selection techniques in machine learning are:

1. Filter Methods: These methods select features based on their statistical properties, such as correlation, chi-square tests, or information gain. Examples include Pearson correlation coefficient, chi-square test, and mutual information.
2. Wrapper Methods: These methods involve selecting subsets of features and evaluating them using a predictive model. Examples include forward selection, backward elimination, and recursive feature elimination.
3. Embedded Methods: These methods incorporate feature selection as part of the model training process. Examples include Lasso (L1 regularization), Ridge (L2 regularization), and decision trees.

Each of these techniques has its advantages and is suited to different types of datasets and machine learning models.