Random Search Parameter Tuning in Python using scikit-learn

(Random Search Parameter Tuning in Python using scikit-learn)


# import python packages
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)

 

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