EvoLinear.jl

ML library implementing linear boosting with L1 and L2 regularization.

For tree based boosting, consider EvoTrees.jl.

Supported loss functions:

• mse: mean squared-error regression

• logloss: logistic regression

• poisson

• gamma:

• tweedie:

Installation

pkg> add https://github.com/jeremiedb/EvoLinear.jl

Getting started

Define a learner with EvoLinearRegressor. This objects holds the hyper-paramters of the model.

Then EvoLinear.fit trains a model defined in the learner on a Tables compatible objects. The features, target and optionally weight variable names must be specified.

using EvoLinear, DataFrames
using EvoLinear: fit

x_train, y_train = rand(1_000, 10), rand(1_000)
dtrain = DataFrame(x_train, :auto)
dtrain.y .= y_train

config = EvoLinearRegressor(loss=:mse, nrounds=10, L1=1e-1, L2=1e-2)
m = fit(config, dtrain; target_name="y", feature_names=["x1", "x3"]);
p = m(dtrain)