MM - Classification - Naïve Bayes, Decision Trees

Creating a mind map with keywords and short sentences for future recall can be an effective way to summarize and visualize the content. Here’s how you can structure your mind map for Naïve Bayes and Decision Trees:

Naïve Bayes

1. Overview

   • Probabilistic Model
   • Classification Tasks
   • Independence Assumption

2. Bayes' Theorem

   • Posterior Probability
   • Likelihood
   • Prior Probability

3. Assumption of Independence

   • Conditional Independence
   • Simplified Calculation

4. Types of Naïve Bayes

   • Gaussian: Continuous Data
   • Multinomial: Discrete Count Data
   • Bernoulli: Binary Data

5. Applications

   • Spam Filtering
   • Text Classification
   • Sentiment Analysis

6. Implementation

   • Import Libraries
   • Fit and Predict
   • Accuracy Score

Decision Trees

1. Overview

   • Non-parametric
   • Classification & Regression
   • Decision Rules

2. Structure

   • Root Node: Entire Dataset
   • Internal Nodes: Attribute Tests
   • Leaf Nodes: Outcome/Class

3. Splitting Criteria

   • Gini Impurity: Measure Impurity
   • Entropy: Measure Randomness

4. Algorithm Steps

   • Select Best Attribute
   • Divide Dataset
   • Recursive Splitting

5. Pruning

   • Reduce Overfitting
   • Remove Weak Sections

6. Applications

   • Predictive Modeling
   • Data Analysis
   • Decision Making

7. Implementation

   • Import Libraries
   • Fit and Predict
   • Visualize Tree

Keywords for Mind Map Nodes

Naïve Bayes:

• Probabilistic Model
• Classification
• Independence Assumption
• Bayes' Theorem
• Posterior, Likelihood, Prior
• Conditional Independence
• Gaussian, Multinomial, Bernoulli
• Spam, Text, Sentiment
• Fit, Predict, Accuracy

Decision Trees:

• Non-parametric
• Decision Rules
• Root, Internal, Leaf Nodes
• Gini Impurity
• Entropy
• Attribute Tests
• Recursive Splitting
• Pruning, Overfitting
• Predictive Modeling
• Visualize Tree

Example Mind Map Layout

Central Node: Classification Algorithms

1. Naïve Bayes

   • Definition: Probabilistic Model
   • Assumption: Independence
   • Types: Gaussian, Multinomial, Bernoulli
   • Applications: Spam, Text, Sentiment
   • Steps: Fit, Predict, Evaluate
   • Key Terms: Posterior, Likelihood, Prior

2. Decision Trees

   • Definition: Non-parametric
   • Structure: Root, Internal, Leaf Nodes
   • Criteria: Gini, Entropy
   • Steps: Select Attribute, Split, Recursion
   • Pruning: Reduce Overfitting
   • Applications: Modeling, Analysis
   • Key Terms: Decision Rules, Splitting, Visualization

You can create a visual representation of this mind map using mind mapping tools like MindMeister, XMind, or even on paper. This structure will help you recall the key concepts and details of Naïve Bayes and Decision Trees effectively.