Skip to content

ML model types for categorization

ML model types that can be used in categorization projects are classified in the following groups:

Support Vector Machine

Support Vector Machines (SVM) models separate data points into groups by a line or n-dimensional spaces of best fit.

The available SVM model types are:

Decision trees ensemble

Decision trees ensemble models generate collections of decision trees (typically if-then branches) and infer decision rules from the data features of the training samples.

These models are an efficient way to combine the predictions of different estimators built with a given learning algorithm.

They can be used to generate multiple decision tree estimators following two different approaches:

The available model types based on decision trees are:


Linear models determine functions that describe the relationship between a dependent variable and one or more independent variables.

The linear model available is Logistic Regression.

Naive Bayes

Naive Bayes models are a family of classification algorithms based on Bayes' Theorem.

Naive Bayes Models assume that the contribution of each feature to a given prediction is independent and equal. This "naive" assumption is far from correct, especially in text-classification (where different textual categorizations are highly correlated), but in specific conditions it could turn out to be a reasonable approximation.

The available Naive Bayes model types are: