Hi Tim,
There isn't a direct implementation of CatBoost for MATLAB. However, there are a few strategies you could consider to potentially improve the performance of your models on high-dimensional data in MATLAB:
Feature Selection/Reduction:
- Use MATLAB's built-in functions for feature selection, such as sequentialfs (sequential feature selection), relieff (ReliefF algorithm), or fscmrmr (Minimum Redundancy Maximum Relevance). Refer to this documentation link: https://in.mathworks.com/help/stats/sequentialfs.html
- Consider dimensionality reduction techniques like PCA (pca function) or t-SNE (tsne function) to reduce the number of variables while retaining most of the variance in the data. Refer to this documentation link: https://in.mathworks.com/help/stats/tsne.html
Ensemble Methods:
- MATLAB's Statistics and Machine Learning Toolbox offers ensemble methods such as random forests (TreeBagger or fitcensemble for classification).
- You can build an ensemble of different models and use a voting scheme to improve predictions. Refer to this documentation link: https://in.mathworks.com/help/stats/select-predictors-for-random-forests.html
Hyperparameter Optimization:
- Use bayesopt or hyperparameters functions for Bayesian optimization to fine-tune the hyperparameters of your models. Refer to this documentation link:https://in.mathworks.com/help/stats/bayesopt.html
Advanced Preprocessing:
- Normalize or standardize your data using normalize or zscore. Refer to this documentation: https://in.mathworks.com/help/matlab/ref/double.normalize.html
- Explore advanced preprocessing techniques like variable clustering or filtering methods to remove noisy features.
Deep Learning:
- For high-dimensional data, deep learning models might be effective. MATLAB's Deep Learning Toolbox provides functions and apps for designing, training, and evaluating deep neural networks. Refer to this documentation link: https://in.mathworks.com/help/deeplearning/referencelist.html?type=function&s_tid=CRUX_topnav
AUC is a good metric for binary classification problems, but you should also consider others such as accuracy, precision, recall, and F1-score for a comprehensive evaluation.
