What is feature selection in neural networks

This recipe explains what is feature selection in neural networks

Recipe Objective - What is feature selection in neural networks.?

Feature selection is a technique in feature engineering of identifying related features from the data and removing the less relevant features from the data which has a huge impact on model performance which can have a positive or a negative impact. Feature selection is defined as a process in which there is a automatic or manual selection of only those features which contribute most to the prediction variable or feature for which model is being build. Feature selection reduces the number of input variables or features in the model build which also helps n reducing the computational cost of model. It enhances shorter training times, avoids curse of dimensionality. Feature selection returns the subset of features and does not create new features from the functions of the original features like feature extraction.

This recipe explains what is Feature selection, how it is beneficial for neural network models and how it can be executed.

A Deep Dive into the Types of Neural Networks

Explanation of Feature Selection.

Feature selection reduces the overfitting in the neural network model as it selects the features from the data that is the model has less redundant data available and that decreases the opportunity to make decisions based on noise. It also improves generalization of the neural network model.

Feature selection improves accuracy of the neural network model as it selects the features from the data that is the model has less misleading data that is data with missing values, NaN values of data mix of binary and categorical values which overfits the model in the later stage and which improves the accuracy of the model on test data and validation data.

Feature selection reduces the training time of the neural network model as it selects the features from the data that is the model has fewer data points and without misleading data so, fewer data points reduces algorithm complexity and thus algorithms train faster that is neural network model trains faster.

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