Morten Hjorth-Jensen
18 lines
703 B
Python
18 lines
703 B
Python
import numpy as np
|
|
from scipy.stats import uniform as sp_rand
|
|
from sklearn import datasets
|
|
from sklearn.linear_model import Ridge
|
|
from sklearn.model_selection import RandomizedSearchCV
|
|
# load the diabetes datasets
|
|
dataset = datasets.load_diabetes()
|
|
# prepare a uniform distribution to sample for the alpha parameter
|
|
param_grid = {'alpha': sp_rand()}
|
|
# create and fit a ridge regression model, testing random alpha values
|
|
model = Ridge()
|
|
rsearch = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100)
|
|
rsearch.fit(dataset.data, dataset.target)
|
|
print(rsearch)
|
|
# summarize the results of the random parameter search
|
|
print(rsearch.best_score_)
|
|
print(rsearch.best_estimator_.alpha)
|