利用sklearn生成分类数据集 sklearn.datasetsimportmake_classification x,y=make_classification(n_samples=10000,n_classes=2,n_features=60,n_informative=30,n_redundant=30,n_clusters_per_class=2,weights=[0.95,],class_sep=2)y[y==1]=-1y[y==0]=1 n_samples: 生成的样本数量,默认值为100。 n_...
fromsklearn.datasetsimportmake_classification X, y=make_classification(n_samples=10000,# 样本个数 n_features=25,# 特征个数 n_informative=3,# 有效特征个数 n_redundant=2,# 冗余特征个数(有效特征的随机组合) n_repeated=0,# 重复特征个数(有效特征和冗余特征的随机组合) n_classes=3,# 样本类别 n...
sklearn.datasets.make_classification( n_samples=100,# 样本个数n_features=20,# 特征个数n_informative=2,# 有效特征个数n_redundant=2,# 冗余特征个数(有效特征的随机组合)n_repeated=0,# 重复特征个数(有效特征和冗余特征的随机组合)n_classes=2,# 样本类别n_clusters_per_class=2,# 蔟的个数weights...
首先，让我们导入必要的库：import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import make_classification, make_regression, make_blobs, make_moons, make_circles, make_s_curve, make_swiss_roll, make_checkerboard1. 生成分类数据集要生成分类数据集，可以使用 make_classification函数。...
datasets.make_friedman1 datasets.make_friedman2 datasets.make_friedman3 datasets.make_gaussian_quantiles datasets.make_hastie_10_2 datasets.make_low_rank_matrix datasets.make_moons datasets.make_multilabel_classification datasets.make_regression
sklearn.datasets.make_hastie_10_2(n_samples=12000,random_state=None) 利用Hastie算法,生成二分类数据 importmatplotlib.pyplotasplt fromsklearn.datasetsimportmake_classification fromsklearn.datasetsimportmake_blobs fromsklearn.datasetsimportmake_gaussian_quantiles ...
from sklearn.datasets import make_regression from sklearn.feature_selection import RFECV from sklearn.linear_model import Ridge # Build a synthetic dataset X, y = make_regression(n_samples=10000, n_features=15, n_informative=10) # Init/fit the selector ...
data,target = datasets.make_classification(n_classes=4,n_samples=1000,n_features=2,n_informative=2,n_redundant=0,n_clusters_per_class=1) print(data.shape) plt.scatter(data[:,0],data[:,1],c=target) plt.show() x,y = datasets.make_regression(n_samples=10,n_features=1,n_targets=1...
from sklearn.datasets import make_classification from imblearn.under_sampling import RandomUnderSampler # 生成一个具有样本不均衡的分类数据集 X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, n_classes=2, weights=[0.05, 0.95], random_state=1337) ...
首先,使用sklearn中的make_classification来生成一些用来分类的样本。 fromsklearn.datasetsimportmake_classification x,y=make_classification(n_samples=1000,n_features=2,n_redundant=0,n_informative=1,n_clusters_per_class=1)#n_samples:生成样本的数量#n_features=2:生成样本的特征数,特...