Plotting Cross-Validated Predictions

Adapted from http://scikit-learn.org/stable/auto_examples/plot_cv_predict.html

This example shows how to use cross_val_predict to visualize prediction errors.

using ScikitLearn
using ScikitLearn.CrossValidation: cross_val_predict

@sk_import datasets: load_boston
@sk_import linear_model: LinearRegression
using PyPlot

lr = LinearRegression()
boston = load_boston()
y = boston["target"]

# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validated:
predicted = cross_val_predict(lr, boston["data"], y, cv=10)

scatter(y, predicted)
plot([minimum(y), maximum(y)], [minimum(y), maximum(y)], "k--", lw=4)
xlabel("Measured")
ylabel("Predicted");