How to use Random Forest Variations

Hi,

right now I'm trying to confirm the results of a scientific paper, which claims to have used a random forest algorithm to fit a certain dataset.

After trying both the TreeBagger and fitrensemble functions, the models don't seem to be able to correctly fit the data.

Kindly help me with the following,

1. How can I improve the model results?
2. Is it possible to modify the algorithm to try different random forest variations?
3. If so, how can I do that?

Any help is much appreciated.

Answer 1

[Prasanna]

Hi Hoang,

It is my understanding that you want to improve a random forest model results and want to know if it is possible to modify the algorithm to try different random forest variations.

To improve the model results of a Random Forest algorithm in MATLAB, you can try the following strategies:

• Hyperparameter Tuning: Adjust various hyperparameters of the Random Forest algorithm, such as the number of trees (‘NumTrees’), the maximum number of decision splits or nodes (‘MaxNumSplits’), minimum leaf size (‘MinLeafSize’), and maximum number of features to consider for a split (‘NumPredictorsToSample’).
• Data Preprocessing: Make sure your data is properly pre-processed. This includes handling missing values, scaling or normalizing features, and encoding categorical variables if necessary.
• Data Augmentation: If the dataset is small, you might consider techniques to artificially expand your dataset, such as SMOTE for imbalanced classification tasks or generating synthetic data points.
• Ensemble Size: Increasing the number of trees in the forest might improve performance, but it will also increase computational cost. There's usually a point of diminishing returns, so use cross-validation to find an optimal number.

To modify the algorithm to try different Random Forest variations, you can play with the hyperparameters in MATLAB using the following functions:

• “fitrensemble”: This function fits an ensemble of learners for regression. It provides more flexibility in terms of the type of ensemble method used ('Bag', 'LSBoost', 'GentleBoost', etc.) and allows you to customize the base learner ('Learners' option).
• “TreeBagger”: This function creates an ensemble of decision trees for classification or regression. You can specify options such as 'Method', 'NumTrees', 'MinLeafSize', 'OOBPrediction', etc.

Make sure to use cross-validation to evaluate the model's performance and avoid overfitting. You can use the following documentations to further check on cross validation tree ensemble models, Random forests, boosted and bagged regression trees:

• https://www.mathworks.com/help/stats/regression-tree-ensembles.html
• https://www.mathworks.com/help/stats/improving-classification-trees-and-regression-trees.html

Hope this helps,

Regards,

Prasanna