Data Science and Machine Learning

Currently Playing: Ep 15 — Machine Learning Cross Validation --- Checking Every Shelf, Not Just One Cross-Validation

#machinelearning #datascience #ml #ai #maths #algortihm #artificialintelligence Core Idea: One test set might mislead — check several. Concept: K-fold cross-validation. Why it matters: A single train/test split can be lucky or unlucky; averaging across multiple folds gives a more honest measure of your model's true performance. Core mechanic: You will demonstrate cross_val_score(model, X, y, cv=5). Example: Showing 5-fold CV scores varying slightly across different splits, then being averaged together. Common mistake: Trusting one single split's score as the final word. The Rule: "One honest read of one shelf isn't the same as knowing the room." Next Clue: A slow, deliberate walk toward one misplaced book (bridging to Ep 16: Gradient Descent).