我正在尝试在 Julia 中对以下数据集执行岭回归，任何人都可以共享资源以在代码中执行岭回归吗？

Question

x={10.9, 12.4, 13.5, 14.6, 14.8, 15.6, 16.2, 17.5, 18.3, 18.6} 和 y={24.8, 30.0, 31.0, 29.3, 35.9, 36.9, 42.5, 37.9, 38.9, 40.5}.

我已经使用代码执行了线性回归：

using DataFrames, CSV
using PyPlot
using Plots
using GLM

x = [10.9, 12.4, 13.5, 14.6, 14.8, 15.6, 16.2, 17.5, 18.3, 18.6] #x-values
y = [24.8, 30.0, 31.0, 29.3, 35.9, 36.9, 42.5, 37.9, 38.9, 40.5]  #Y-values

data = DataFrame(X=x, Y=y)

linearRegressor = lm(@formula(Y ~ X), data)

println(linearRegressor)

linearFit = predict(linearRegressor)
(plot!(x, linearFit,))
display(scatter!(x, y))

Answer 1

按照 Bogumil 在评论中的建议使用 MLJLinearModels

julia> x = [10.9, 12.4, 13.5, 14.6, 14.8, 15.6, 16.2, 17.5, 18.3, 18.6];

julia> y = [24.8, 30.0, 31.0, 29.3, 35.9, 36.9, 42.5, 37.9, 38.9, 40.5];

julia> xₘ = reshape(x, length(x), 1); # since MLJ will expect x to be a Matrix (2d array)

julia> using MLJLinearModels

julia> fit(RidgeRegression(), xₘ, y)
2-element Vector{Float64}:
 2.2488474986814606
 0.24878206004726794

我正在尝试在 Julia 中对以下数据集执行岭回归，任何人都可以共享资源以在代码中执行岭回归吗？

I'm trying to perform ridge regression on the following dataset in Julia, Can anyone share resources to perform ridge regression in code?

julia

linear-regression