Random Forest is an ensemble machine learning algorithm used for classification and regression that combines multiple decision trees to enhance accuracy and reduce overfitting. It is robust, handles large datasets effectively, and provides insights into feature importance but requires significant computational resources and can be slower than individual decision trees. Key applications include credit scoring, medical diagnosis, and stock market prediction, with implementation readily available in Python using libraries like scikit-learn.

1. Random Forest: An Overview
Seminar Presentation

2. Introduction to Random Forest
• • Random Forest is a versatile machine
learning algorithm.
• • It is an ensemble method, primarily used for
classification and regression.
• • Combines multiple decision trees to improve
accuracy.

3. What is Random Forest?
• • A collection of decision trees.
• • Trees are trained on different parts of the
same dataset.
• • Final output is based on the majority vote or
average prediction.

4. How Random Forest Works?
• • Create multiple decision trees.
• • Combine the results from these trees.
• • Reduce the risk of overfitting.

5. Key Features of Random Forest
• • High Accuracy
• • Robustness to overfitting
• • Handles large datasets effectively
• • Provides feature importance

6. Applications of Random Forest
• • Credit scoring
• • Medical diagnosis
• • Stock market prediction
• • Recommendation systems

7. Advantages of Random Forest
• • Easy to use and understand
• • Works well with missing data
• • Less prone to overfitting compared to
individual decision trees

8. Disadvantages of Random Forest
• • Requires more computational resources
• • Slower to predict than individual decision
trees
• • Can be less interpretable

9. Random Forest vs. Decision Trees
• • Random Forest reduces the risk of
overfitting
• • Decision Trees are simpler and faster for
small datasets
• • Random Forest is more accurate but
complex

10. Feature Importance in Random
Forest
• • Random Forest provides insights into which
features are most important
• • Feature importance is determined by the
contribution of each feature to the prediction

11. Out-of-Bag Error in Random Forest
• • OOB error is an internal validation metric
• • Calculated using data not included in the
bootstrap sample
• • Provides an unbiased estimate of the model
error

12. Tuning Hyperparameters in
Random Forest
• • Number of trees (n_estimators)
• • Maximum depth of the trees
• • Minimum samples required to split a node
• • Number of features considered at each split

13. Random Forest Implementation in
Python
• • Scikit-learn provides a simple
implementation
• • Fit the model with 'RandomForestClassifier'
or 'RandomForestRegressor'
• • Predict using the trained model
• • Evaluate using accuracy, precision, recall,
etc.

14. Real-World Example of Random
Forest
• • Predicting patient outcomes using medical
data
• • Identifying fraudulent transactions in
banking
• • Recommending products in an e-commerce
platform

15. Challenges with Random Forest
• • Requires large amounts of computational
power
• • Not as interpretable as other models
• • Requires careful tuning of hyperparameters

16. Conclusion
• • Random Forest is a powerful and versatile
algorithm
• • It is widely used in various fields
• • Proper tuning and understanding can lead to
robust models

17. References
• • Breiman, L. (2001). Random Forests.
Machine Learning.
• • Scikit-learn documentation: https://scikit-
learn.org/
• • Hands-On Machine Learning with Scikit-
Learn, Keras, and TensorFlow by Aurélien
Géron