What Wrapper Method Meaning, Applications & Example
A feature selection technique that evaluates model performance.
What is a Wrapper Method?
A Wrapper Method is a feature selection technique used in machine learning to improve the performance of a model by selecting a subset of features that contribute the most to the model’s predictive accuracy. It evaluates different subsets of features by training a model using them and selecting the best-performing set.
How Wrapper Methods Work
- Subset Evaluation: Wrapper methods generate different subsets of features and evaluate the performance of the model on each subset.
- Search Algorithms: Common search strategies like forward selection, backward elimination, and genetic algorithms are used to explore different feature combinations.
- Model Training : For each subset of features, a model is trained and tested. The subset with the best performance is selected.
Types of Wrapper Methods
- Forward Selection: Starts with no features and adds the most important feature at each step based on model performance.
- Backward Elimination: Starts with all features and iteratively removes the least important feature.
- Genetic Algorithms: Uses evolutionary techniques like crossover and mutation to find the optimal set of features.
Applications of Wrapper Methods
- Feature Selection: Wrapper methods are primarily used for selecting relevant features in high-dimensional datasets, ensuring that the model only uses the most significant features.
- Model Optimization: By using only the most relevant features, wrapper methods can improve model performance and reduce overfitting .
- Data Preprocessing: Wrapper methods help in preparing datasets for modeling by ensuring the features used provide the most value.
Example of a Wrapper Method
In a classification task for customer churn prediction, a wrapper method like forward selection might start with no features, then add features such as “age,” “usage frequency,” and “customer service calls.” At each step, the model is evaluated based on performance metrics (e.g., accuracy), and the best feature subset is selected to optimize the model’s predictive power.