Workflow Automation Templates
A library of ready-to-use workflow templates to accelerate your data journey
Feature Selection Methods
Identify key features for better models
Overview
This workflow demonstrates different feature selection techniques to identify the most relevant variables for predicting life expectancy. It improves model accuracy, reduces overfitting, and simplifies computation.
Details
The process begins by loading the WHO Life Expectancy dataset, renaming columns, and cleaning data by removing null values. The Feature Selection with Importance node ranks variables based on their predictive power using a linear regression model, while the Feature Selection with Correlation node identifies the top correlated features with the target variable Life_Expectancy.
Both methods help in selecting the five most influential features, which are displayed using Print N Rows nodes for comparison.
This workflow highlights how statistical and model-based selection methods enhance efficiency and interpretability in predictive modeling.