Machine Learning Concepts Explained #4: Features and Labels

1. 1Define features as input variables in a dataset
2. 2Identify labels as target output variables in a dataset
3. 3Distinguish between features and labels in a sample dataset
4. 4Apply feature scaling and normalization techniques to prepare data for modeling
5. 5Use labeled data to train a supervised machine learning model

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

💡 Features are input variables, while labels are target output variables, and both are crucial for training supervised machine learning models

🤖 Features vs Labels: Know the difference to build effective supervised #MachineLearning models