Random Forest is a powerful algorithm in Machine Learning. It is based on the Ensemble Learning technique (bagging). Following are the advantages and disadvantages of Random Forest algorithm.
Advantages of Random Forest
1. Random Forest is based on the bagging algorithm and uses Ensemble Learning technique. It creates as many trees on the subset of the data and combines the output of all the trees. In this way it reduces overfitting problem in decision trees and also reduces the variance and therefore improves the accuracy.
2. Random Forest can be used to solve both classification as well as regression problems.
3. Random Forest works well with both categorical and continuous variables.
4. Random Forest can automatically handle missing values.
5. No feature scaling required: No feature scaling (standardization and normalization) required in case of Random Forest as it uses rule based approach instead of distance calculation.
6. Handles non-linear parameters efficiently: Non linear parameters don't affect the performance of a Random Forest unlike curve based algorithms. So, if there is high non-linearity between the independent variables, Random Forest may outperform as compared to other curve based algorithms.
7. Random Forest can automatically handle missing values.
8. Random Forest is usually robust to outliers and can handle them automatically.
9. Random Forest algorithm is very stable. Even if a new data point is introduced in the dataset, the overall algorithm is not affected much since the new data may impact one tree, but it is very hard for it to impact all the trees.
10. Random Forest is comparatively less impacted by noise.
Disadvantages of Random Forest
1. Complexity: Random Forest creates a lot of trees (unlike only one tree in case of decision tree) and combines their outputs. By default, it creates 100 trees in Python sklearn library. To do so, this algorithm requires much more computational power and resources. On the other hand decision tree is simple and does not require so much computational resources.
2. Longer Training Period: Random Forest require much more time to train as compared to decision trees as it generates a lot of trees (instead of one tree in case of decision tree) and makes decision on the majority of votes.
Advantages of Random Forest
1. Random Forest is based on the bagging algorithm and uses Ensemble Learning technique. It creates as many trees on the subset of the data and combines the output of all the trees. In this way it reduces overfitting problem in decision trees and also reduces the variance and therefore improves the accuracy.
2. Random Forest can be used to solve both classification as well as regression problems.
3. Random Forest works well with both categorical and continuous variables.
4. Random Forest can automatically handle missing values.
5. No feature scaling required: No feature scaling (standardization and normalization) required in case of Random Forest as it uses rule based approach instead of distance calculation.
6. Handles non-linear parameters efficiently: Non linear parameters don't affect the performance of a Random Forest unlike curve based algorithms. So, if there is high non-linearity between the independent variables, Random Forest may outperform as compared to other curve based algorithms.
7. Random Forest can automatically handle missing values.
8. Random Forest is usually robust to outliers and can handle them automatically.
9. Random Forest algorithm is very stable. Even if a new data point is introduced in the dataset, the overall algorithm is not affected much since the new data may impact one tree, but it is very hard for it to impact all the trees.
10. Random Forest is comparatively less impacted by noise.
Disadvantages of Random Forest
1. Complexity: Random Forest creates a lot of trees (unlike only one tree in case of decision tree) and combines their outputs. By default, it creates 100 trees in Python sklearn library. To do so, this algorithm requires much more computational power and resources. On the other hand decision tree is simple and does not require so much computational resources.
2. Longer Training Period: Random Forest require much more time to train as compared to decision trees as it generates a lot of trees (instead of one tree in case of decision tree) and makes decision on the majority of votes.
very good explanation and precise
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