Machine Learning Concepts Explained #4: Features and Labels

1. 1Define features as input variables in a dataset
2. 2Identify labels as target variables in a dataset
3. 3Distinguish between features and labels in a sample dataset
4. 4Apply feature engineering techniques to improve model performance
5. 5Use labels to evaluate model accuracy and make predictions

Data scientists and machine learning engineers benefit from understanding features and labels to build accurate models

💡 Features are input variables, while labels are target variables that models learn to predict

🤖 Features vs Labels: Know the difference to build better ML models!