emel v0.1.2 API Reference
Modules
Turn data into functions! A simple and functional machine learning library written in elixir
A mathematical function conceived as a model of biological neurons. Receives one or more separately weighted inputs, sums them and passes the sum through an activation function to produce an output
Uses a decision tree to go from observations about an item (represented in the branches) to conclusions about the item’s discrete target value (represented in the leaves)
Aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean
A non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space
A linear approach to modelling the relationship between a dependent variable and one or more independent variables
A simple probabilistic classifier based on applying Bayes’ theorem with naive independence assumptions between the features. It makes classifications using the maximum posteriori decision rule in a Bayesian setting
A binary classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector