LinearRegressor
LinearRegressor
A model type for constructing a linear regressor, based on MultivariateStats.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
LinearRegressor = @load LinearRegressor pkg=MultivariateStats
Do model = LinearRegressor()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in LinearRegressor(bias=...)
.
LinearRegressor
assumes the target is a Continuous
variable and 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, aDataFrame
) whose columns are of scitypeContinuous
; check the column scitypes withschema(X)
.y
is the target, which can be anyAbstractVector
whose element scitype isContinuous
; check the scitype withscitype(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 featuresXnew
, which should have the same scitype asX
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
LinearRegressor = @load LinearRegressor pkg=MultivariateStats
linear_regressor = LinearRegressor()
X, y = make_regression(100, 2) ## a table and a vector (synthetic data)
mach = machine(linear_regressor, X, y) |> fit!
Xnew, _ = make_regression(3, 2)
yhat = predict(mach, Xnew) ## new predictions
See also MultitargetLinearRegressor
, RidgeRegressor
, MultitargetRidgeRegressor