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Random forest algorithm
The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.
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Random forest algorithm
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- 2. INTRODUCTION Random forest algorithmis a supervised classification algorithm. More trees in the forest the more robust the forest looks like. Same way in random forest classifier, the higher the number of trees in the forest gives the high accuracy results.
- 3. INTRODUCTION IN SIMPLE WORDS Randomforest builds multiple decision trees And mergers them together to get a more accurate And stable prediction
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- 5. How Random ForestAlgorithm Works Data Data Subset 1 Data Subset 2 Data Subset 3 Data Subset n
- 6. How Random ForestAlgorithm Works
- 7. Random Forest pseudocode Randomlyselect “k” features from total “m” features. K<m Among “k” features, calculate the node “d” using the best split point. Split the node into daughter modes using the best split. Repeat 1 to 3 steps until “I” number of nodes has been reached. Build forest by repeating steps 1 to 4 for “n” number times to create “n” number of trees.
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- 9. ADVANTAGES The overfittingproblem will never come when we use the random forest algorithm in any classification problem. It can be used for both classification and regression task.
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