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MultitargetLinearRegressor

MultitargetLinearRegressor

A model type for constructing a multitarget linear regressor, based on MultivariateStats.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

MultitargetLinearRegressor = @load MultitargetLinearRegressor pkg=MultivariateStats

Do model = MultitargetLinearRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in MultitargetLinearRegressor(bias=...).

MultitargetLinearRegressor assumes the target variable is vector-valued with continuous components. It trains a linear prediction function using the least squares algorithm. Options exist to specify a bias term.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

Here:

  • X is any table of input features (eg, a DataFrame) whose columns are of scitype Continuous; check column scitypes with schema(X).
  • y is the target, which can be any table of responses whose element scitype is Continuous; check the scitype with scitype(y).

Train the machine using fit!(mach, rows=...).

Hyper-parameters

  • bias=true: Include the bias term if true, otherwise fit without bias term.

Operations

  • predict(mach, Xnew): Return predictions of the target given new features Xnew, which should have the same scitype as X above.

Fitted parameters

The fields of fitted_params(mach) are:

  • coefficients: The linear coefficients determined by the model.
  • intercept: The intercept determined by the model.

Examples

using MLJ
using DataFrames

LinearRegressor = @load MultitargetLinearRegressor pkg=MultivariateStats
linear_regressor = LinearRegressor()

X, y = make_regression(100, 9; n_targets = 2) ## a table and a table (synthetic data)

mach = machine(linear_regressor, X, y) |> fit!

Xnew, _ = make_regression(3, 9)
yhat = predict(mach, Xnew) ## new predictions

See also LinearRegressor, RidgeRegressor, MultitargetRidgeRegressor