Pesquisa aleatória cv
# Import necessary modules
from scipy.stats import randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import RandomizedSearchCV
# Setup the parameters and distributions to sample from: param_dist
param_dist = {"max_depth": [3, None],
"max_features": randint(1, 9),
"min_samples_leaf": randint(1, 9),
"criterion": ["gini", "entropy"]}
…# Print the tuned parameters and score
print("Tuned Decision Tree Parameters: {}".format(tree_cv.best_params_))
print("Best score is {}".format(tree_cv.best_score_))
josh.ipynb