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What is How to Train and Validate ML Models about?

This document outlines the process of training machine learning models, emphasizing the importance of data preparation, model evaluation, and the concepts of overfitting, bias, and variance. It explains the necessity of splitting datasets into training and test sets, tuning hyperparameters, and using cross-validation to ensure reliable performance estimates. Additionally, it discusses the trade-off between bias and variance, the characteristics of models suffering from high bias or variance, and the evaluat

Author

khansanazeer03

Language

EN