What Is a Machine Learning Model?

A machine learning model is a program that is used to make predictions for a given data set. A machine learning model is built by a supervised machine learning algorithm and uses computational methods to “learn” information directly from data without relying on a predetermined equation. More specifically, the algorithm takes a known set of input data and known responses to the data (output) and trains the machine learning model to generate reasonable predictions for the response to new data.

Table of Contents

Types of Machine Learning Models
Machine Learning Models with MATLAB
Integrate Machine Learning Models into Systems

Tutorials and examples

Types of Machine Learning Models

There are two main types of machine learning models: machine learning classification (where the response belongs to a set of classes) and machine learning regression (where the response is continuous).

Choosing the right machine learning model can seem overwhelming—there are dozens of classification and regression models, and each takes a different approach to learning. This process requires evaluating tradeoffs, such as model speed, accuracy, interpretability, and complexity, and can involve trial and error.

The following is an overview of machine learning classification and regression machine learning models to help you get started.

Machine learning models for classification and regression with MATLAB.

Popular Machine Learning Models for Classification or Regression

Machine Learning Model	How Machine Learning Model Works	Machine Learning Model Representation
Support Vector Machine (SVM)	An SVM classifies data by finding the linear decision boundary (hyperplane) that separates data points of one class from data points of the other class. The best hyperplane for an SVM has the largest margin between the two classes when the data is linearly separable. If the data is not linearly separable, a loss function penalizes points on the wrong side of the hyperplane. SVMs sometimes use a kernel transformation to project nonlinearly separable data into higher dimensions where a linear decision boundary can be found. SVM regression algorithms work like SVM classification algorithms, but the regression algorithms are modified to predict continuous responses. They find a model that deviates from the measured data with minimal parameter values to reduce sensitivity to errors.	Classification
Support Vector Machine (SVM)		Regression
Decision Tree	A decision tree lets you predict responses to data by following the decisions in the tree from the root (beginning) down to a leaf node. A tree consists of branching conditions where the value of a predictor is compared to a trained weight. The number of branches and the values of weights are determined in the training process. Additional modification, or pruning, may be used to simplify the model.
Ensemble Trees	In ensemble methods, several weaker decision trees are combined into a stronger ensemble. A bagged decision tree consists of trees that are trained independently on bootstrap samples of the input data. Boosting involves iteratively adding and adjusting the weight of weak learners. There is an emphasis on misclassified observations or fitting new learners to minimize the mean-squared error between the observed response and the aggregated prediction of all previously grown learners.
Generalized Additive Model (GAM)	GAM models explain class scores or response variables using a sum of univariate and bivariate shape functions of predictors. These models use a shape function, such as a boosted tree, for each predictor and, optionally, each pair of predictors. The shape function can capture a nonlinear relation between predictors and predictions.
Neural Network	Inspired by the human brain, a neural network consists of interconnected nodes or neurons in a layered structured that relate the inputs to the desired outputs. The machine learning model is trained by iteratively modifying the strengths of the connections so that given inputs map to the correct response. The neurons between the input and output layers of a neural network are referred to as hidden layers. Shallow neural networks typically have few hidden layers. Deep neural networks have more hidden layers than shallow neural networks. They can have hundreds of hidden layers. Neural networks can be configured to solve classification or regression problems by placing a classification or regression output layer at the end of the network. For deep learning tasks, such as image recognition, you can use pretrained deep learning models. Common types of deep neural network are CNNs and RNNs.

Popular Machine Learning Models for Classification

Machine Learning Model	How Machine Learning Model Works	Machine Learning Model Representation
Naive Bayes	A naive Bayes classifier assumes that the presence of a feature in a class is independent of other features. It classifies new data based on the highest probability of it belonging to a particular class. This probability is determined by the probabilities of each feature.
k-Nearest Neighbor (KNN)	KNN is a type of machine learning model that categorizes objects based on the classes of their nearest neighbors in the data set. KNN predictions assume that objects near each other are similar. Distance metrics, such as Euclidean, city block, cosine, and Chebyshev, are used to find the nearest neighbor.
Discriminant Analysis	Discriminant analysis classifies data by finding linear combinations of features. The analysis assumes that different classes generate data based on Gaussian distributions. Training the machine learning model involves finding the parameters of a Gaussian distribution for each class. Boundaries, either linear or quadratic, are calculated based on these parameters. These boundaries are used to classify new data.

Popular Machine Learning Models for Regression

Machine Learning Model	How Machine Learning Model Works	Machine Learning Model Representation
Linear Regression	Linear regression is a statistical modeling technique used to describe a continuous response variable as a linear function of one or more predictor variables. Because linear regression models are simple to interpret and easy to train, they are often the first models to try when working with a new data set.
Nonlinear Regression	Nonlinear regression is a statistical modeling technique that helps describe nonlinear relationships in experimental data. These models are generally assumed to be parametric, described as nonlinear equations. Nonlinear refers to a fit function that is a nonlinear function of the parameters. For example, if the fitting parameters are b0, b1, and b2: the equation y = b₀+b₁x+b₂x² is a linear function of the fitting parameters, whereas y = (b₀x^b1)/(x+b₂) is a nonlinear function of the fitting parameters.
Generalized Linear Model	A generalized linear model (GLM) is a special case of nonlinear models that uses linear methods. The inputs are transformed by a nonlinear link function such as a logarithm or logit function. The linear combination of transformed inputs is solved using a linear best fit. The logistic regression machine learning model is an example of a GLM.
Gaussian Process Regression (GPR)	GPR models are nonparametric machine learning models used for predicting the value of a continuous response variable. The response variable is modeled as a random Gaussian process, using covariances with each input variable. The machine learning model also models the uncertainty of the response. These models are widely used in spatial analysis for interpolation in the presence of uncertainty. GPR is also referred to as Kriging.

Machine Learning Models with MATLAB

Using MATLAB^® with Statistics and Machine Learning Toolbox™, you can train many types of machine learning models for classification and regression. The following tables list MATLAB functions that create popular machine learning models and documentation topics, which describe how the machine learning models work. With MATLAB, you can create more machine learning models than the ones listed here.

Machine Learning Models for Classification

Machine Learning Model	MATLAB Function that Creates Machine Learning Model	How Machine Learning Model Works in MATLAB
SVM	`fitcsvm`
Naive Bayes	`fitcnb`
KNN	`fitcknn`
Decision Tree	`fitctree`
Decision Tree Ensemble	`fitcensemble`
Discriminant Analysis	`fitcdiscr`
GAM	`fitcgam`
Shallow Neural Network	`fitcnet`
Deep Neural Network	`trainNetwork`

Machine Learning Models for Regression

Machine Learning Model	MATLAB Function that Creates Machine Learning Model	How Machine Learning Model Works in MATLAB
Linear Regression	`fitlm`
Nonlinear Regression	`fitnlm`
GPR	`fitrgp`
SVM	`fitrsvm`
Generalized Linear Model	`fitglm`
Tree	`fitrtree`
Regression Tree Ensemble	`fitrensemble`
GAM	`fitrgam`
Shallow Neural Network	`fitrnet`
Deep Neural Network	`trainNetwork`

MATLAB provides low-code apps (Classification Learner (4:34) and Regression Learner (3:42)) for designing, tuning, assessing, and optimizing machine learning models.

Classification Learner App

Regression Learner App

Specialized apps for interactively exploring data, selecting features, and training, comparing, and assessing machine learning models.

With MATLAB, you can automate the process of building optimized machine learning models. Using autoML techniques, you can streamline data exploration and preprocessing, feature extraction and selection, machine learning model selection and tuning, and the preparation of a machine learning model for deployment.

Integrate Machine Learning Models into System

Using MATLAB and Simulink^® with Statistics and Machine Learning Toolbox, you can integrate machine learning models into the design and simulation of complex AI-driven systems, such as a reduced-order model of a vehicle engine.

SVM machine learning model integrated into Simulink system by using a ClassificationSVMPredict block with one input and two outputs.

Integrate a machine learning model into a Simulink system by using a dedicated block.

For each machine learning model, a dedicated Simulink block exists. The following table lists Simulink blocks and relevant examples for integrating classification and regression machine learning models into system designs.

Simulink Blocks for Classification Models

Machine Learning Model	Simulink Block for Machine Learning Model	How To Integrate Machine Learning Model in Simulink System
SVM	ClassificationSVM Predict
KNN	ClassificationKNN Predict
Decision Tree	ClassificationTree Predict
Decision Tree Ensemble	ClassificationEnsemble Predict
Shallow Neural Network	ClassificationNeuralNetwork Predict
Deep Neural Network	Predict

Simulink Blocks for Regression Models

Machine Learning Model	Simulink Block for Machine Learning Model	How To Integrate Machine Learning Model in Simulink System
Linear Regression	RegressionLinear Predict
GPR	RegressionGP Predict
SVM	RegressionSVM Predict
Regression Tree	RegressionTree Predict
Regression Tree Ensemble	RegressionEnsemble Predict
Shallow Neural Network	RegressionNeuralNetwork Predict
Deep Neural Network	Predict