Difference Between Feature Selection and Feature Extraction

Feature selection and feature extraction are two key techniques used in machine learning to improve model performance by handling irrelevant or redundant features. While both works on data preprocessing, feature selection uses a subset of existing features whereas feature extraction transforms data into a new feature. In this article we will learn more about their key differences.

• Feature selection: involves selecting a subset of the most relevant features that are actually contributing in prediction while discarding the rest features. This helps improve reducing overfitting and increased accuracy. Common techniques include filter, wrapper and embedded methods.
• Feature extraction: transforms existing features into a new set of features that captures better underlying patterns in data. It is useful when raw data is in high dimension or complex. Techniques like PCA, LDA and Autoencoders are used for this purpose.

Key Benefits of both

1. Improved Model Performance: Reduces noise and retains only necessary features leading to higher accuracy and lower error rates.
2. Reduced Overfitting: Prevents model to be overly complex and ensures that it captures meaningful patterns rather than memorizing dataset.
3. Faster Model Training and Inference: Helps in dimensionality reduction of dataset hence make faster training and reduced computational cost.
4. Improved Interpretability: Simplifies the model by focusing on significant features making it easier to understand.

While both has similar benefits their working are totally different.

Difference Feature Selection and Feature Extraction Methods

Feature selection and feature extraction methods have their own advantages and disadvantages depending on the nature of the data and the task they handle. 

Feature selection picks the most relevant features, while feature extraction transforms data into new representations. The choice depends on the dataset, problem complexity and model requirements.