Why feature selection/extraction is required?

Feature selection/extraction is an important step in many machine-learning tasks, including classification, regression, and clustering. It involves identifying and selecting the most relevant features (also known as predictors or input variables) from a dataset while discarding the irrelevant or redundant ones. This process is often used to improve the accuracy, efficiency, and interpretability of a machine-learning model.

Here are some of the main reasons why feature selection/extraction is required in machine learning:

1. Improved Model Performance: The inclusion of irrelevant or redundant features can negatively impact the performance of a machine learning model. Feature selection/extraction can help to identify the most important and informative features, which can lead to better model performance, higher accuracy, and lower error rates.
2. Reduced Overfitting: Including too many features in a model can cause overfitting, where the model becomes too complex and starts to fit the noise in the data instead of the underlying patterns. Feature selection/extraction can help to reduce overfitting by focusing on the most relevant features and avoiding the inclusion of noise.
3. Faster Model Training and Inference: Feature selection/extraction can help to reduce the dimensionality of a dataset, which can make model training and inference faster and more efficient. This is especially important in large-scale or real-time applications, where speed and performance are critical.
4. Improved Interpretability: Feature selection/extraction can help to simplify the model and make it more interpretable, by focusing on the most important features and discarding the less important ones. This can help to explain how the model works and why it makes certain predictions, which can be useful in many applications, such as healthcare, finance, and law.

Machine learning models require input features that are relevant and important to predict the outcome. However, not all features are equally important for a prediction task, and some features might even introduce noise in the model. Feature selection and feature extraction are two methods to handle this problem. In this article, we will explore the differences between feature selection and feature extraction methods in machine learning.