Скачать или смотреть Why You Need 3 Datasets: Visualizing Train, Test, & Validation Splits

We ditch the confusing Venn diagrams and use the interactive visualizations to show exactly why splitting your data two ways (Train/Test) is a recipe for overfitting. We demonstrate the "Pet Classification" experiment to prove why you need a third "Validation" set to tune hyperparameters without cheating.

Key Takeaways:

The 3-Split Rule: Visualizing the strict boundaries between Training (Learning patterns), Validation (Tuning models), and Testing (Final "in the wild" check). * The "Peeking" Trap: Why looking at your Test Set performance before the very end is a cardinal sin in Data Science that ruins your model's credibility.

Interactive Decision Boundaries: Watching how a Logistic Regression decision line shifts in real-time as we drag "Cat" and "Dog" data points across the graph.

Validation vs. Test: A clear visual breakdown of why a model might win on Validation data but fail on Test data—and why that's actually a good reality check.

Feature Selection in Real-Time: Comparing how single-feature models (Weight only) vs. multi-feature models (Weight + Fluffiness) behave on unseen data.

Hashtags: #MachineLearning #DataScience #ModelValidation #Overfitting #TrainTestSplit #Python #DataVisualization #Algorithms #DeepLearning