Types of Machine Learning Models Explained (2024)

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

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 asimage recognition, you can usepretrained deep learning models. Common types of deep neural network are CNNs and RNNs.

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.

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+b₂x²is 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.

SVM

Naive Bayes

KNN

Decision Tree

Decision Tree Ensemble

Discriminant Analysis

GAM

Shallow Neural Network

Deep Neural Network

Linear Regression

Nonlinear Regression

GPR

SVM

Generalized Linear Model

Tree

Regression Tree Ensemble

GAM

Shallow Neural Network

Deep Neural Network

SVM

ClassificationSVM Predict

KNN

ClassificationKNN Predict

Decision Tree

ClassificationTree Predict

Decision Tree Ensemble

ClassificationEnsemble Predict

Shallow Neural Network

ClassificationNeuralNetwork Predict

Deep Neural Network

Predict

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