What Feature Selection Meaning, Applications & Example
Process of choosing relevant features for model training.
What is Feature Selection?
Feature Selection is the process of identifying the most relevant variables or features from a dataset to improve model accuracy and reduce computational costs. By selecting only the most meaningful features, Feature Selection enhances model performance and interpretability , especially in high-dimensional datasets.
Types of Feature Selection
- Filter Methods: Rank features based on statistical measures, like correlation or chi-square tests, independent of any learning algorithm .
- Wrapper Methods: Use iterative testing with a specific model to select the best feature subset (e.g., forward selection, backward elimination).
- Embedded Methods: Perform feature selection during the model training process, such as in Lasso regression.
Applications of Feature Selection
- Text Classification: Reduces noise by selecting relevant words or phrases, improving classification accuracy.
- Medical Diagnosis: Identifies the most critical biomarkers from a large set of patient data to assist in disease prediction.
- Financial Analysis: Selects key financial indicators that impact stock or market predictions.
Example of Feature Selection
In a credit scoring model, Feature Selection might identify income, age, and credit history as the most influential variables for predicting a customer’s likelihood to default, reducing unnecessary data and improving model efficiency.