What is Cross Validation? | K-fold Cross Validation | Stratified K-fold Cross Validation скачать в хорошем качестве

What is Cross Validation? | K-fold Cross Validation | Stratified K-fold Cross Validation

📚 In this video, we introduce, the concept of cross-validation 🌐 We kick off by explaining the importance of validation in model training, covering the traditional approach of splitting historical data into training and testing sets. 🔄 However, there is a flaw in this method i.e. the randomness of test data selection. As the test data changes, so does the training set, leading to variable model performance. Enter the game-changer — K-fold cross-validation. 🎲 We break down the concept, illustrating how this technique mitigates the inconsistency by running multiple train-test iterations with different data folds. 🎓 Taking it a step further, we address a common issue in classification problems. What if the division of data creates an imbalance in the target column across folds? Here's where we introduce Stratified K-fold cross-validation, ensuring a balanced representation for more accurate model evaluation. 🖥️ Our video isn't just about theory; we bring the concepts to life with a hands-on Python guide. 🐍 Follow along as we walk through each step, providing a clear parallel between theory and practical implementation. 🤓 This video intends to equip you with a comprehensive understanding of cross-validation, empowering you to elevate your model evaluation game. 🚀 Happy Learning!