Model building
Use these stored procedures to build machine learning models. You can build a machine learning model using popular algorithms such as linear regression, decision tree classifier, naive bayes, and k-means clustering.
Decision trees
A decision tree is a hierarchical, graphical structure that helps you carefully examine and accurately classify a model.
Linear regression
Linear regression is the most commonly used method of predictive analysis. It uses linear relationships between a dependent variable (target) and one or more independent variables (predictors) to predict the future of the target. The prediction is based on the assumption that the relationship between the target and the predictors is dependent or causal.
Naive Bayes
The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions.
K-means clustering
The K-means algorithm is the most widely used clustering algorithm that uses an explicit distance measure to partition the data set into clusters.