Chapter 2
Refining Your Model
MATLAB® removes a lot of the hassle of figuring out which model works best with its Classification Learner app. You can also use MATLAB to judge how well your model is performing, and verify your results.
Using Classification Learner, you can perform common machine learning tasks such as interactively exploring your data, selecting features, specifying validation schemes, training multiple models in parallel, and assessing results.
With the app you can:
- Assess classifier performance using confusion matrices, ROC curves, or scatter plots
- Compare model accuracy using the misclassification rate on the validation set
- Improve model accuracy with advanced options and feature selection
- Export the best model to the workspace to make predictions on new data
- Generate MATLAB code to train classifiers on new data
Statistics and Machine Learning Toolbox Apps
Find the Classification Learner app, along with the apps for your other installed products, in the Apps tab in the MATLAB Toolstrip.
Classification Learner App Supports:
Discriminant analysis classifiers
Linear, quadratic
Decision trees
Fine tree, medium tree, coarse tree
Support vector machines
Linear SVM, fine Gaussian SVM, medium Gaussian SVM, coarse Gaussian SVM, quadratic SVM, cubic SVM, linear, quadratic
Nearest neighbor classifiers
Fine KNN, medium KNN, coarse KNN, cosine KNN, cubic KNN, weighted KNN
Naive Bayes classifiers
Gaussian NB, Kernel NB
Ensemble classifiers
Boosted trees (AdaBoost, RUSBoost), bagged trees, subspace KNN, subspace discriminant
Neural Network classifiers
Narrow, Medium, Wide, Bilayered, Trilayered
Kernel Approximation classifiers
SVM Kernel, Logistic Regression
Logistic regression classifier
