Ensemble Learning in Machine Learning​ | Decision Tree & Random Forest | Machine Learning Tutorial

Ensemble Learning in Machine Learning​ | Decision Tree & Random Forest | Machine Learning Tutorial
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Hey everyone, in this Machine learning tutorial, we are learning about the decision tree, a powerful and intuitive machine learning model. We will explore how these tree like structures make decisions and then we'll take it a step further by learning about powerful ensemble methods like random forest. This is a live tutorial we've compiled as an in-depth decision tree guide just for you. By the end of this video, you will have a solid understanding of these techniques and how to apply them to real world problems.

Objectives:
1. Decision Trees: Working principles, advantages over logistic regression, and handling multiple features.
2. Ensemble Methods: Boosting, stacking, and bagging, with a focus on Random Forests.
3. Random Forests: Combining weak decision trees, robustness, and reduced overfitting.
4. Practical Implementation: Preprocessing, hyperparameter tuning, and performance evaluation.
5. Model Optimization: Cross-validation, grid search, and hyperparameter tuning.

Topics Covered
Chapter 1 - Decision Tree & Random Forest
00:00:39 - Decision Trees And Ensembles
00:01:45 - Curves VS Corners
00:06:25 - Decision Trees Using a Single Feature
00:10:24 - What is a Decision Tree?
00:21:51 - General Representation of Decision Tree
00:23:20 - Sklearn Output of Decision Tree
00:26:19 - Using Decision Trees for Classification
00:32:46 - Decision Trees using Multiple Features
00:36:50 - Categorical Features in Decision Tree
00:41:08 - What is Gini Index?
00:59:58 - Multiclass Classification Using Decision Trees
01:06:45 - Advantages and Disadvantages of Decision Tree
01:11:43 - Hyperparameters in Decision Trees
01:21:51 - Decision Tree Classification In Sklearn
01:23:00 - SetupTree Model
01:35:00 - Unrestricted Tree Models
01:51:16 - Restricted Tree Models
01:56:55 - Hyperparameter Tuning

Chapter 2 : Ensemble Methods
02:12:20 - What is Ensembles
02:14:12 - Ensemble Methods
02:15:02 - Revising Decision Trees
02:18:11 - Advantages and Disadvantages of Decision Tree
02:22:07 - Ensemble Paradigms
02:23:28 - Types of Ensemble Methods
02:38:49 - Intution Behind bagging
02:48:54 - Training the Individual Learners
02:51:27 - Note on Randomness
02:52:57 - Sources of Randomness in Random Forests
02:55:33 - Note on Out-of-Bag Error
02:59:35 - Out-of-Bag Error - Computation Algorithm
03:00:59 - Hyperparameters in Random Forests
03:03:08 - Random Forest Classification In Sklearn
03:06:21 - Cross-validation in Supervised Learning
03:09:03 - Evaluating Supervised ML Model
03:09:57 - 2-Fold Cross Validation
03:13:05 - 3-Fold Cross Validation
03:16:20 - GridSearchCV
03:23:51 - What is Refit?
03:26:05 - What is Scoring?
03:33:04 - Example on Basic Random Forest Model
03:47:58 - Example on Restricted Random Forest Model
03:53:26 - Example on Tree Depth
04:01:10 - Combinations of Hyperparameters
04:11:45 - Other Performance Metrics

#decisiontree #Randomforest #machinelearning

By the end of this Decision Trees And Ensembles Model, you'll have a solid understanding of how Decision Trees and Ensemble methods work, and how to apply them effectively in your machine learning projects.