Train Test Split and Stratification in ML | Chapter 9 Machine Learning Tutorial скачать в хорошем качестве

Train Test Split and Stratification in ML | Chapter 9 Machine Learning Tutorial

Welcome to Chapter 9 of our Machine Learning tutorial series using Scikit-Learn. In this video, we explain how to **split data into training and testing sets**, a critical step in building reliable machine learning models. Proper data splitting ensures that models are evaluated fairly and can generalize well to unseen data. Topics covered in this chapter include: 1. Why Train Test Splitting Matters Understand the importance of separating data for training and evaluation. Learn how improper splitting can lead to overfitting and misleading results. 2. Using train_test_split Learn how to use *train_test_split* to divide datasets into training and testing sets. Understand parameters such as test size, random state, and shuffle. Practical examples for classification and regression problems. 3. Stratification Learn what *stratification* means and why it is important for imbalanced datasets. Understand how stratified splitting preserves class distribution. Practical examples using classification datasets with uneven class labels. 4. Common Mistakes and Best Practices Avoiding data leakage during splitting. Choosing the right split ratio for different dataset sizes. 5. Practical Demonstrations Apply train test splitting on real datasets. Compare model performance with and without stratification. By the end of this chapter, you will understand how to correctly split your data into training and testing sets, apply stratification when needed, and prepare your datasets for fair and accurate model evaluation in machine learning workflows. Useful Links: GitHub: https://github.com/Ezee-Kits/ YouTube:    / @ezee_kits   Email: ezeekits@gmail.com #Python #MachineLearning #ScikitLearn #DataScience #PythonTutorial #LearnPython #TrainTestSplit #Stratification #MLForBeginners #PythonProgramming #AI #DataAnalysis #DataVisualization #MLTutorial #PythonProjects #SoftwareDevelopment #Tech #PredictiveModeling