Machine learning algorithms use computational methods to "learn" information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.
Classification algorithms enable you to model a categorical response variable as a function of one or more predictors. Statistics Toolbox offers a wide variety of parametric and nonparametric classification algorithms, such as:
You can evaluate goodness of fit for the resulting classification models using techniques such as:
Statistics Toolbox offers multiple algorithms to analyze data using k-means, hierarchical clustering, Gaussian mixture models, or hidden Markov models. When the number of clusters is unknown, the toolbox offers cluster evaluation techniques to determine the number of clusters present in the data based on a specified metric.
Cluster Genes Using K-Means and Self-Organizing Maps (Example)
Learn how to detect patterns in gene expression profiles by examining gene expression data
Cluster Analysis (Example)
Use k-means and hierarchical clustering to discover natural groupings in data.
Regression algorithms enable you to model a continuous response variable as a function of one or more predictors. Statistics Toolbox offers a wide variety of parametric and nonparametric classification algorithms, such as:
Computational Statistics: Feature Selection, Regularization, and Shrinkage with MATLAB
In this webinar, you will learn how to use Statistics Toolbox to generate accurate predictive models from data sets that contain large numbers of correlated variables.