Embedded Methods
Embedded methods are a feature selection technique in data science that involves selecting a subset of relevant features from a larger set of features. This technique involves training a machine learning model on the entire set of features and then selecting the most important features based on their contribution to the model's performance. Embedded methods are different from filter methods, which select features based on their statistical properties, and wrapper methods, which select features by training and evaluating multiple models with different subsets of features. Embedded methods are particularly useful when dealing with high-dimensional data, where the number of features is much larger than the number of observations. By selecting only the most important features, embedded methods can improve the performance of machine learning models and reduce overfitting.
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