100 Days Challenge Day 10 - Decision Trees and Random Forests

100 Days Challenge - Day 10.

Decision Trees and Random Forests

Pic from towardsdatascience site

Learnt about Decision Trees and Random Forests from StatQuest Youtube channel

Topics covered include:
Decision Trees

• Terminology (Root, Node, Leaf)
• Gini impurity and Building a Tree for Categorica/Numeric/Ranked data
• Feature selection and impurity threshold to prevent overfitting.
• Filling Missing Values

Random Forests

• Disadvantages of Decision Trees (Inefficiency with new samples)
• Bootstrapping, Bagging
• Evaluation (Using Out-Of-Bag dataset)
• Handling missing values in the original data
• Proximity Matrix and Distance Matrix
• Handling missing values in the data we want to classify

Sources:

StatQuest: Decision Trees

StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data

StatQuest: Random Forests Part 1 - Building, Using and Evaluating

StatQuest: Random Forests Part 2: Missing data and clustering