Simulate data from the Friedman 1 benchmark problem. These data were originally described in Friedman (1991) and Breiman (1996). For details, see sklearn.datasets.make_friedman1.
gen_friedman(
n_samples = 100,
n_features = 10,
n_bins = NULL,
sigma = 0.1,
seed = NULL
)A data frame of simulated observations from the Friedman 1 benchmark problem.
Integer specifying the number of samples (i.e., rows) to generate. Default is 100.
Integer specifying the number of features to generate. Default is 10.
Integer specifying the number of (roughly) equal sized bins to
split the response into. Default is NULL for no binning. Setting to
a positive integer > 1 effectively turns this into a classification problem
where n_bins gives the number of classes.
Numeric specifying the standard deviation of the noise.
Integer specifying the random seed. If NULL (the default)
the results will be different each time the function is run.
Breiman, Leo (1996) Bagging predictors. Machine Learning 24, pages 123-140.
Friedman, Jerome H. (1991) Multivariate adaptive regression splines. The Annals of Statistics 19 (1), pages 1-67.