01 Decision Tree Regression (Theory)
- Non parametric algo
- find descriptive features contain most information about target
- split those feature to get pure subset
- learn simple decision rule of a target variable
02 Classification Tree (Theory)
- Decision tree used for Classification Problem
03 Entropy, Information Gain & Gini Index (Theory)
- Which feature to select first
- Entropy measure purity of split :
- [-p(one class) * log(p(one class))] - [p(second class) * log(p(second class))]
- Information Gain is collection of all entropy from root node to leaf node
- Gini Index : Calculate purity of split also
- 1- [P^2]
- Gini Impurity ranges from 0 - 0.5, Entropy ranges from 0 - 1
04 Decision Tree Classification (Theory)
- Decision tree used for Classification Problem
05 Decision Tree Classification (Python Code)
- Step by Step Python code to visualize Regression Tree
06 Decision Tree Classification (Python Code)
- Step by Step Python code to visualize Classification Tree
07 Random Forest and Ensemble Technique (Theory)
- Bagging Technique
- Collection of Decision Tree
- Variable Importance measure
08 Voting Classifier (Theory)
- Hard Voting : Predict output class with highest majority of Voting
- Soft Voting : Predict output class with average of probability given to the class
09 Random Forest (Python Code)
- Step by Step Python code for Random Forest Tree
10 Grid Search (Python Code)
- Hyperparameter Tuning
- Cross Validation
- Grid Search with Cross Validation
11 Interview Question Decision Tree & Random Forest