Ensemble Learning | Random Forest, AdaBoost, Gradient Boosting | Day 20 of Data Science in 30 Days

One decision tree scored 91.2% on real cancer data. Soft voting over three different models scored 99.1% on the same split. In Day 20 of Data Science in 30 Days we cover ensemble learning: why combining models works, and how to do it with bagging, random forests, AdaBoost, gradient boosting, voting and stacking, in Python, with real results. 💡You will learn: Why a vote of many models beats each single model, and why diversity matters Bagging, bootstrap samples and the free out-of-bag score Random Forest, feature importance, and why importance can mislead AdaBoost and Gradient Boosting, learning rate and early stopping Soft voting and stacking (99.1% test accuracy, 98.1% in cross-validation) 3 mistakes to avoid (boosting with no plan, imbalanced data, skipping the simple baseline) A cheat sheet and 5 interview questions 💻CODE (everything in the video, runs top to bottom): https://drive.google.com/file/d/1omhY... 🟧 FREE RESOURCES: 🔸scikit-learn ensembles guide: https://scikit-learn.org/stable/modul... 🔸Permutation importance: https://scikit-learn.org/stable/modul... 🔸Early stopping in Gradient Boosting: https://scikit-learn.org/stable/auto_... 🔸Random Forests (Breiman, 2001): https://doi.org/10.1023/A:1010933404324 🔸XGBoost docs: https://xgboost.readthedocs.io/en/sta... 🔸LightGBM docs: https://lightgbm.readthedocs.io/en/la... 🔸CatBoost docs: https://catboost.ai/docs/ StatQuest, Random Forests Part 1:    • StatQuest: Random Forests Part 1 - Buildin...   🔸StatQuest, Gradient Boost Part 1:    • Gradient Boost Part 1 (of 4): Regression M...   🗃️ DATASET: Breast Cancer Wisconsin (Diagnostic), included in scikit-learn. Also on UCI: https://archive.ics.uci.edu/dataset/1... TOOLS: Python, scikit-learn, NumPy. ▶️ CHAPTERS: 00:00 Intro: 100 trees, one vote 01:40 Quick recap (Day 19) 02:25 Why ensembles work 03:35 1. Bagging (bootstrap + out-of-bag score) 04:46 2. Random Forest 06:10 Feature importance (and its trap) 07:05 3. AdaBoost 08:15 4. Gradient Boosting 09:55 XGBoost, LightGBM, CatBoost 10:35 Bagging vs boosting 11:12 Decision boundaries on noisy data 12:12 5. Voting and Stacking 13:40 Python code (scikit-learn) 15:00 Results and cross-validation 16:25 Key hyperparameters 17:55 3 mistakes to avoid 18:35 Cheat sheet 19:10 Interview questions 19:55 Recap and Day 21 preview ⚠️ DISCLAIMER: Educational content using a public research dataset. These models are for learning and are not medical advice or a diagnostic tool. PREVIOUS VIDEO: Day 19, Classification in Machine Learning    • Classification in Machine Learning | Logis...   FULL SERIES: Data Science in 30 Days    • Data Science in 30 Days | Data Science Ful...   Next: Day 21, Hyperparameter Tuning (Grid Search and Random Search). ♥️ Subscribe to The Data Key for more such content related to data science and artificial intelligence world. -----------------------------------------------------------------