MATLAB machine learning opens doors to building powerful predictive models in a variety of applications, from computer vision to medical diagnosis and financial risk prediction. Machine learning algorithms learn from data and adapt to improve their performance, making them valuable tools in modern data science. MATLAB provides a complete environment where you can develop these models from the ground up without relying on pre-built solutions alone.
This tutorial will guide you through building your first predictive model using the MATLAB Statistics and Machine Learning Toolbox. You will know the following once you complete this piece:
- How to set up and configure the MATLAB machine learning toolbox
- How to build a predictive model from scratch using MATLAB machine learning functions
- How to test and verify your model’s performance
Let us begin!
Setting Up MATLAB Machine Learning Toolbox
Before building predictive models, you need the Statistics and Machine Learning Toolbox installed in your MATLAB environment. This toolbox requires MATLAB as a prerequisite and runs on Mac, Windows, and Linux platforms. You have two main options when installing: the Add-On Manager within MATLAB or the standalone MATLAB installer.
Open MATLAB and type ver in the Command Window to verify successful installation. The toolbox should appear in the list of installed products. You now have access to functions and apps for statistics, visualizations, clustering, and regression.
The toolbox provides supervised, semi-supervised, and unsupervised machine learning algorithms. These include support vector machines (SVMs), boosted decision trees, and k-means clustering. You can use the Classification Learner app or Regression Learner app to build models interactively. These apps train multiple models and help you select the best performer automatically. Programmatic workflows using AutoML give you command-line control as an alternative.
Data loading happens through simple commands. Use load filename to bring datasets into your workspace if files are stored locally. The webread function fetches remote datasets directly from URLs without downloading files first. MATLAB has sample datasets like fisheriris.mat and carbig.mat available right after installation to practice.
Building Your First Predictive Model from Scratch
Creating predictive models in MATLAB follows a consistent workflow in any method. Start by cleaning your data. Remove outliers and treat missing values. After you prepare the data, identify whether your problem requires classification (categorical outcomes) or regression (continuous predictions). You then preprocess data into a suitable format and specify training and testing subsets. Train model parameters and conduct performance tests. Confirm accuracy on unseen data and deploy if results satisfy requirements.
Classification Learner and Regression Learner apps train multiple model types at once for interactive model development. Classification Learner supports decision trees, support vector machines, logistic regression, nearest neighbors, ensemble methods and neural network classifiers. Regression Learner offers linear regression models, regression trees, Gaussian process regression, support vector machines and ensemble methods. Both apps apply cross-validation by default to prevent overfitting. You can generate MATLAB code from trained models.
Programmatic training gives you more control. Use fitclinear to handle high-dimensional binary classification with regularized SVM or logistic regression. fitcsvm trains SVM models with kernel functions for low to moderate-dimensional data. Each approach requires balancing tradeoffs between model speed and accuracy.
Testing and Validating Model Performance
Model validation determines whether your predictions generalize beyond training data. MATLAB provides several approaches to partition datasets for testing. The cvpartition function creates random partitions with options for holdout validation, k-fold cross-validation, or stratified sampling. A typical 70-30 split uses cvpartition(n,'Holdout',0.3) where n represents your observation count. randperm generates random indices for manual splitting, while dividerand offers straightforward percentage-based partitioning.
Cross-validation prevents overfitting by testing models on unseen data. K-fold validation uses crossval to partition your trained model into k subsets and then calculates performance across all folds. The kfoldLoss function returns the average error rate across folds. Holdout validation splits data into training and test sets for classification tasks, where the software computes accuracy on held-out observations.
The Classification Learner app displays validation accuracy in the Models pane and highlights the best performer. You can get into confusion matrices, ROC curves, and precision-recall curves to assess classification performance. Regression models use mean squared error through the loss function on test data. Hyperparameter optimization requires separate test sets to avoid overfitting since optimization itself can lead to models tuned too much to validation data.
Conclusion
I encourage you to experiment with the Classification Learner and Regression Learner apps using MATLAB’s built-in datasets to solidify what we’ve covered here.
This tutorial walked you through setting up the Statistics and Machine Learning Toolbox, building predictive models both interactively and programmatically, and proving performance right using cross-validation techniques. These foundational skills will enable you to tackle ground prediction challenges with confidence. Enjoy your MATLAB machine learning experience!
