Feature Selection
2024 | AI Dictionary
What is Feature Selection: Process of choosing relevant features for model training, improving model accuracy and reducing overfitting.
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.
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