In general, random forest models are quite robust to outliers and don’t require much data pre-processing. However, there are a few things to keep in mind when working with random forest models.
Random forest models are based on the decision tree algorithm. Decision trees are very sensitive to outliers and can be easily swayed by a few extreme values. For this reason, it’s generally a good idea to normalize your data before training a decision tree model.
Random Forest models create multiple decision trees, each of which is trained on a different subset of the data. This means that the individual decision trees are less likely to be influenced by outliers. However, if the number of outliers is large enough, they can still have an impact on the model as a whole.
In general, you should always normalize your data before training any machine learning model. However, you may not need to normalize your data before training a random forest model. If you have a lot of outliers in your data, or if you’re not sure whether or not your data is normalized, it’s best to err on the side of caution and normalize your data before training the model.
Related reading: Petrified Forest
When is normalization not recommended in random Forest?
When it comes to random forests, normalization is not always recommended. This is because normalization can sometimes lead to overfitting, which can reduce the overall accuracy of the model. Additionally, normalization can also make the model more difficult to interpret.
Frequently Asked Questions
Is feature scaling necessary for random forest normalization?
No, feature scaling is not necessary for random forest normalization.
Would you recommend feature normalization when using boosting trees?
Yes, I recommend feature normalization when using boosting trees.
How important is the translation for random forest features?
This question is impossible to answer without more information about your dataset and the task at hand.
Is feature scaling necessary for random forest?
No, feature scaling is not necessary for random forest. Random Forest is a tree-based model and hence does not require feature scaling.
Why doesn't the random forest scale with partition size?
For the same reason that scaling is unnecessary: the random forest looks for partitions, and partitions only depend on how the data are sorted.
Sources
- https://www.datasciencelearner.com/does-random-forest-need-normalization-complete-analysis/
- https://www.kdnuggets.com/2022/07/random-forest-algorithm-need-normalization.html
- https://ml-concepts.com/2021/08/13/how-data-normalization-affects-your-random-forest-algorithm-by-javier-fernandez-towards-data-science/
- https://www.kaggle.com/questions-and-answers/86923
- https://careerfoundry.com/en/blog/data-analytics/what-is-random-forest/
- https://stackoverflow.com/questions/57339104/is-normalization-necessary-for-randomforest
- https://www.quora.com/Why-is-it-important-to-normalize-features-when-running-a-random-forest-or-any-other-ensemble-learning-method
- https://corporatefinanceinstitute.com/resources/knowledge/other/random-forest/
- https://www.researchgate.net/publication/322766311_Random_forest_meteorological_normalisation_models_for_Swiss_PM10_trend_analysis
- https://community.rapidminer.com/discussion/10117/normalisation-changes-random-forest-performance
- https://medium.com/analytics-vidhya/evaluating-a-random-forest-model-9d165595ad56
- https://www.datasciencelearner.com/how-to-improve-accuracy-of-random-forest-classifier/
- https://acp.copernicus.org/articles/18/6223/2018/acp-18-6223-2018.pdf
- https://towardsdatascience.com/random-forest-regression-5f605132d19d
- https://www.quora.com/How-could-I-regularize-random-forest-classifiers
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