Tree-Based-Models-in-Python

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Tree-Based-Models-in-Python

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Tree based algorithm in machine learning including both theory and codes. Topics including from decision tree regression and classification to random forest tree and classification. Grid Search is also included.

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# Machine-Learning-with-Tree-Based-Models-in-Python

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

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