What Is The Purpose Of Feature Selection In Data Analytics?

Feature selection in data analytics is done for the motive to eliminate unnecessary noise around the useful insights in data analytics. With the help of feature selection our clients get exactly what they were looking to from an available dataset and it also improves the model performance and accuracy.

In this blog, we will get familiar with the purpose and benefits of feature selection in data analytics in detail.

What Is Feature Selection?

Feature Selection is a process of identifying and extracting the most relevant and crucial features or insights from the available data. It is mainly used to build machine learning models, market analysis, predictive analysis and more.

With feature selection we can extract more exact insights from the available data making more meaningful usage of data analytics.
It helps to reduce complexity of feature models extracted from the data.
The cost incurred is also less in training models with effective feature analysis techniques that are more subtle to occur at cheaper computational cost.
Feature selection helps to prevent poor irrelevant features to be loaded into the models and hence avoid overfitting.

Working of Feature Selection In Data Analytics

Let us understand the working of feature selection in data analytics.

First a list of features are selected and arranged based on their priorities using statistical methods such as chi-squared, correlation, tree-based models, etc.
After ranking a subset of the most important feature is selected.
Now, the relevance of these features is evaluated for a machine learning algorithm.
The wrapper method is used to test and train the model with different combinations of features.
With forward selection the most important relevant features are added iteratively until no further improvement is required.
With backward elimination the least relevant features are discarded iteratively.

What Is the Purpose of Feature Selection in Data Analytics?

feature selection in data analytics

Some of the most important purposes of using feature selection in data analytics are mentioned below.

1. Improves Model Accuracy

The model selected using feature selections are more appropriate with better predictive performance and focuses on the most important variables in the data and hence better model accuracy and efficiency.

2. Reduces Overfitting

With Feature selection implementation we will experience no more overhead of unnecessary data or highly redundant or similar features in the list. It prevents the model from loading and training unnecessary model data.

3. Faster Computational Speed

As the data insights using feature selection is accurate and to the point the model will take lesser time as compared to the heavy loaded dataset with even low memory usage that triggers faster computation. It is beneficial for large and more complex real world applications.

4. Enhances Interpretability

This is made possible due to a lesser and more confined number of features selection from the dataset. It helps in identifying the most important factors which influence the predictions in the dataset.

5. Removes Multicollinearity

By avoiding unnecessary amounts of data in the model it will make sure that it is stable and reliable with model coefficients in linear regression models.

Common Feature Selection Techniques In Data Analytics

There are many feature selection techniques that are being used in data analytics and machine learning to improve the model performance.

Using Filter Methods

This feature selection method is also known as Statistical Techniques used to evaluate the importance of features independently with no dependency on the model.

Chi-Square Test: It is used for categorical variables to test independence between different features.
Correlation Matrix: It removes the highly correlated features in the dataset.
Variance Threshold: It removes features with low variance as they will provide very less information in the dataset.

Using Wrapper Methods

This Feature selection method in data analytics is also known as model based selection where it evaluates the subsets of features by training and testing the model using different combinations.

Recursive Feature Elimination: It recursively removes the least important features until the best subset feature is found from the dataset.
Forward Selection: This method is used to start all features and remove the least significant one by one from the set.
Backward Elimination: This feature is used to start with all features and removes the least significant one by one.

Using Embedded Methods

This feature selection in data analytics method is used to perform feature selection while training the models.

Lasso Regression: It shrinks less important features weights to zero and effectively removes them. It is also known as L1 Regularization.
Ridge Regression: It is used to reduce the effect of the less important features but does not eliminate them. It is also known as L2 Regularization
Decision Trees: It is used to assign scores to features based on their contribution and importance.

Using Dimensionality Reduction Techniques

These techniques are used to transform the dataset into small feature space while retaining the essential information in the data.

Principal Component Analysis (PCA): It reduces the feature space by finding the new uncorrelated dimensions.
Linear Discriminant Analysis (LDA): This feature is similar to PCA however it works best with labeled data used for classification.

Difference Between Feature Generation and Feature Selection in Data Science

Back end technologies

Some major differences between feature generation and feature selection in data analytics are mentioned in the table below.

Feature Generation	Feature Selection
It deals with creating new features from existing data to improve model performance.	It is used for choosing the most relevant features from the dataset to reduce complexity.
It enhances the predictive power by deriving new insights.	It is used to remove irrelevant, redundant, or noisy features for better efficiency.
Some common examples or techniques used are Feature engineering, transformations, domain knowledge-based creation.	Some common examples or techniques used are statistical tests, wrapper methods, embedded techniques, dimensionality reduction.
It is used for creating “Age Group” from “Age”, extracting “Day of the Week” from a date column.	It is used for removing highly correlated variables, selecting top 10 features using Recursive Feature Elimination (RFE).
It can improve model accuracy by providing new informative features.	It helps in reducing overfitting, improving model speed, and interpretability.
It consists of polynomial features, binning, one-hot encoding, feature interactions.	It consists of Lasso regression, PCA, correlation filtering, mutual information.
When raw features are insufficient or need transformation for better learning.	When the dataset has too many features, leading to computational inefficiency or overfitting.

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Feature Selection in Data Analytics FAQs

Q1. What is Feature Selection in data analytics?

Ans: Feature Selection is a process of identifying and extracting the most relevant and crucial features or insights from the available data. It is mainly used to build machine learning models, market analysis, predictive analysis and more.

Q2. What is the need of feature selection in data analysis?

Ans: Feature selection helps to extract the most important insights from the data and eliminates the unnecessary information thus making the system more efficient and lightheaded. It also helps reduce the training and testing of the machine learning model.