Decision Tree Tutorial Notes

Introduction

• Presenter: Richard Kirschner, SimplyLearn
• Topic: Decision trees in machine learning
• Context: Decision-making process, example of buying a car.

What is Machine Learning?

• Part of artificial intelligence (AI).
• Helps in making smarter decisions by analyzing data.
• Basic Premises:
  • Learn
  • Predict
  • Decide
• Applications in various fields: facial recognition, driver recognition, automated car recognition.

Types of Machine Learning

1. Supervised Learning:
   • Data with known answers (e.g., past loan repayment history).
   • Predict future outcomes based on historical data.
2. Unsupervised Learning:
   • Data without known answers.
   • Groups similar data together (e.g., categorizing images).
3. Reinforcement Learning:
   • Learns from receiving feedback on actions taken.
   • Adjusts based on whether actions are classified as good or bad.

Problems in Machine Learning

• Classification Problems: Categorical solutions (yes/no, true/false).
• Regression Problems: Predict continuous values (e.g., price, profit).
• Clustering Problems: Identify patterns in data to group similar items.

Decision Trees

• Definition: Tree-shaped diagram used for decision-making.
• Each branch: Represents possible decisions or reactions.
• Applications: Classification (yes/no decisions) and regression (predictions of numerical values).

Advantages of Decision Trees

• Simple to understand and interpret.
• Minimal data preparation required.
• Handles both numerical and categorical data.
• Non-linear relationships do not affect performance.

Disadvantages of Decision Trees

• Overfitting: Captures noise in the data, leading to poor generalization.
• High variance: Can be unstable with small data variations.
• Low bias: Complex trees can struggle with new data.

Key Terms

• Entropy: Measure of randomness/unpredictability in the dataset.
• Information Gain: Measure of decrease in entropy after a dataset split.
• Leaf Node: Carries classification/decision at the end of the tree.
• Decision Node: Has two or more branches.
• Root Node: Topmost node in the tree.

Mechanics of Decision Trees

1. Start with a high entropy dataset.
2. Split the dataset to reduce entropy (maximize information gain).
3. Continue splitting until entropy reaches 0.
4. Each split decision is based on conditions that produce the highest information gain.

Use Case: Loan Repayment Prediction

1. Data Preparation: Import necessary Python libraries (e.g., numpy, pandas, sklearn).
2. Load Data: Import the loan repayment dataset.
3. Data Exploration: Use pandas to explore and visualize the dataset.
4. Model Training:
   • Split data into training and testing sets.
   • Build and train the decision tree classifier.
5. Prediction: Use the trained model for predicting loan repayments.
6. Accuracy: Evaluate model accuracy using accuracy score.
   • Example accuracy: 93.67%.

Conclusion

• Recap of key points covered in the tutorial:
  • Understanding machine learning and its types.
  • Problems solved by machine learning.
  • Mechanics and use of decision trees.
• Encouragement to visit SimplyLearn for further questions or to access datasets.