Data Preparation in Machine Learning: 4 Key Steps

Danica Simic on Instagram: "Here's how to actually evaluate your ML model Data leakage, overfitting and underfitting are real problems you'll face when training ML models, especially when data and features are unbalanced A rigorous process ensures your model generalizes and that reported metrics reflect real performance. Let's talk about it 1. Data Splitting Separate train, validation, and test sets to mirror deployment conditions. For temporal data, use time-based splits to prevent future infor

Data Validation Testing | Tools and Techniques

What is Training Data? | IBM

Introduction to Data