Random forest Algorithm using machine learnig.pptx

This ppt is about random forest algorithm

1. Random Forests Algorithm By: Md Enamullah RA2332241030042 21-02-2024

2. Outline What is Random Forests? Algorithm How does Algorithm wor k? Case Study Diagram Advantage Disadvantage

3. What is Random Forests An ensemble classifier using many decision tree models. Can be used for classification or Regression. Accuracy and variable importanceinformation is provided with the results.

4. The Algorithm Let the number of training cases be N, and the number ofvariables in the classifier be M . The number m of input variables are used to determine thedecision at a node of the tree; m should be much less than M . Choose a training set for this tree by choosing N times withreplacement from all N available training cases. Use the rest of thecases to estimate the error of the tree, by predicting their classes . For each node of the tree, randomly choose m variables on whichto base the decision at that node. Cálculate the best split based onthese m variables in the training set . Each tree is fully grown and not pruned.

5. How does Random Forests Algorithm work

6. Case Study

7. Bagging at Training time Training set N subsets (with replacement)

8. Bagging at inference time A test sample 75% confidence

9. Random Subspace Method at training time

10. Random Subspace Method at inference time A test sample 66% confidence

11. Advantage It produces a highly accurate classifier and learning is fast It is capable of handling large datasets with high dimensionality . It can handle thousands of input variables without variable deletion . It enhances the accuracy of the model and prevents the overfitting issue . It offers an experimental method for detecting variable interactions .

12. Disadvantage Although random forest can be used for both classification and regression tasks, it is not more suitable for Regression tasks.

13. THANK YOU…