#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树,精度递增显著)fromsklearnimportdatasets X, y= datasets.make_classification(n_samples=10000,n_features=20,n_informative=15,flip_y=.5, weights=[.2, .8])importnumpy as np training= np.random.choice([True, False], p=[.8, ....
#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树,精度递增显著)fromsklearnimportdatasets X, y= datasets.make_classification(n_samples=10000,n_features=20,n_informative=15,flip_y=.5, weights=[.2, .8])importnumpy as np training= np.random.choice([True, False], p=[.8, ....
#调整随机森林的参数(调整n_estimators随机森林中树的数量默认10个树，精度递增显著)from sklearn import datasets X, y = datasets.make_classification(n_samples=10000,n_features=20,n_informative=15,flip_y=.5, weights=[.2, .8])import numpy as np training = np.random.choice([True, False], p=...
#调整随机森林的参数(调整max_features,结果未见明显差异) 2016-03-31 18:10 −#调整随机森林的参数(调整max_features,结果未见明显差异) from sklearn import datasets X, y = datasets.make_classification(n_samples=10000,n_features=20,n_informative=15,... ...