random_state

使用随机森林做特征选择

fromsklearn.ensembleimportRandomForestClassifierfeat_lables=trainx.columnsforest=RandomForestClassifier(n_estimators=10000,random_state
LightsUpW·2018-07-04 17:12

python实现基本的机器学习算法系列（2）：logstic回归

fromsklearn.datasetsimportmake_blobsfromsklearn.model_selectionimporttrain_test_splitimportmatplotlib.pyplotaspltimportnumpyasnpX,y_=make_blobs(n_samples=10000,n_features=2,centers=2)#,random_state
Kerrwy·2018-06-28 23:09

Sklearn 随机划分训练集和测试集

X_train,X_test,y_train,y_test=cross_validation.train_test_split(train_data,train_target,test_size=0.4,random_state
love music.·2018-06-25 21:10

Python中的逻辑回归（Logistic Regression）函数

classsklearn.linear_model.LogisticRegression(penalty='l2',dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1,class_weight=None,random_state
小白的进阶·2018-05-30 17:13

sklearn的train_test_split

X_train,X_test,y_train,y_test=cross_validation.train_test_split(train_data,train_target,test_size=0.3,random_state
JamesLi6·2018-05-15 19:07

王家林人工智能AI第19课：使用决策树在Social Network上构建汽车销售推荐系统老师微信13928463918

SocialNetwork上构建汽车销售推荐系统老师微信13928463918决策树中的熵entropy：classifier=DecisionTreeClassifier(criterion='entropy',random_state
段智华·2018-05-12 21:55

scikit-learn用train_test_split随机划分数据集和训练集

随机客观的划分数据，减少人为因素完整模板：train_X,test_X,train_y,test_y=train_test_split(train_data,train_target,test_size=0.3,random_state
不论如何未来很美好·2018-05-11 15:53

学习笔记：使用python将数据集划分成测试集和训练集

Python3.6，numpy1.14.2+mkl调用sklearn库的划分函数如下其中train_data为训练输入的数据，train_target为数据类别，test_size为测试集占数据集的比例，random_state
folk_·2018-05-05 19:53

shuffle

X_train,X_test,y_train,y_test=cross_validation.train_test_split(all_train_data,all_label,test_size=0.4,random_state
fkyyly·2018-04-18 00:10

sklearn的make_circles和make_moons生成数据

article/details/53649330make_circles：sklearn.datasets.make_circles(n_samples=100,shuffle=True,noise=None,random_state
YangWei_19·2018-04-14 23:17

Python笔记——cross_validation模块、train_test_split

random_state相当于随机数种子，你可以先看一下图片中的代码和运行结果来了解它的作用。图中设置了random.seed()就相当于在SVC中设置了random_state。
Burgess_ni·2018-04-07 15:05

Sklearn的train_test_split用法

语法X_train,X_test,y_train,y_test=cross_validation.train_test_split(X,y,test_size,random_state)参数说明CodeTextX
fxlou·2018-01-28 20:15

Python机器学习库sklearn.model_selection模块的几个方法参数

sklearn库可以解决的问题：train_test_split返回切分的数据集train/testtrain_test_split(*array,test_size=0.25,train_size=None,random_state
Young_win·2018-01-16 19:28

(sklearn)逻辑回归linear_model.LogisticRegression用法

classsklearn.linear_model.LogisticRegression(penalty=’l2’,dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1,class_weight=None,random_state
MVincent·2017-11-10 14:11

(sklearn)ElasticNet回归 sklearn.linear_model.ElasticNet用法

normalize=False,precompute=False,max_iter=1000,copy_X=True,tol=0.0001,warm_start=False,positive=False,random_state
MVincent·2017-11-09 16:44

(sklearn)lasso回归linear_model.Lasso()方法

normalize=False,precompute=False,copy_X=True,max_iter=1000,tol=0.0001,warm_start=False,positive=False,random_state
MVincent·2017-11-09 15:21

(sklearn)岭回归 sklearn.linear_model.Ridge用法

classsklearn.linear_model.Ridge(alpha=1.0,fit_intercept=True,normalize=False,copy_X=True,max_iter=None,tol=0.001,solver=’auto’,random_state
MVincent·2017-11-09 10:35

sklearn.linear_model中的LogisticRegression

classsklearn.linear_model.LogisticRegression(penalty=’l2’,dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1,class_weight=None,random_state
__gyl__·2017-08-22 20:18

sklearn.model_selection.KFold

K折交叉验证：sklearn.model_selection.KFold(n_splits=3,shuffle=False,random_state=None)思路：将训练/测试数据集划分n_splits
每天进步一点点2017·2017-07-10 10:47

scikit learn 中pca 的用法

classsklearn.decomposition.PCA(n_components=None,copy=True,whiten=False,svd_solver='auto',tol=0.0,iterated_power='auto',random_state
Jiede1·2017-03-19 11:34

RandomizedSearchCV和GridSearchCV，在调用fit方法的时候产生'list' object has no attribute 'values'错误之处理方法

SplitthedatasetintwoequalpartsX_train,X_test,y_train,y_test=train_test_split(data,label,test_size=0.25,random_state
glanose·2017-02-24 10:41

画图工具matplotlib简单实用--绘制散点图

fromsklearn.datasets.samples_generatorimportmake_blobsimportmatplotlib.pyplotaspltX,y=make_blobs(n_samples=100,centers=3,n_features=2,random_state
_飞奔的蜗牛_·2016-12-14 00:11

将数据集切分成“训练-测试数据集”和交叉验证

fromsklearn.cross_validationimporttrain_test_splitXd_train,Xd_test,y_train,y_test=train_test_split(X_d,y,random_state
chloezhao·2016-12-07 11:04

将数据集切分成“训练-测试数据集”和交叉验证

fromsklearn.cross_validationimporttrain_test_splitXd_train,Xd_test,y_train,y_test=train_test_split(X_d,y,random_state
chloezhao·2016-12-07 11:04

将数据集切分成“训练-测试数据集”和交叉验证

fromsklearn.cross_validationimporttrain_test_splitXd_train,Xd_test,y_train,y_test=train_test_split(X_d,y,random_state
Chloezhao·2016-12-07 11:00

Kaggle(2)：验证和过分拟合

1、随机森林的参数在Scikitlearn中使用RandomForestClassifier()进行随机森林分类，其中参数random_state为生成随机数的种子，n_estimators为随机森林的数目
elecjack·2016-03-10 23:00

example datasets

核方法最终的使命是：unfoldthehalf-moonsfromsklearn.datasetsimportmake_moons X,y=make_moons(n_samples=200,shuffle=True,random_state
lanchunhui·2016-01-10 20:00

numpy 常用api（三）

numpy常用api（一）numpy常用api（二）一个函数提供random_state的关键字参数（keywordparameter）：是为了结果的可再现性（reoccurrence）或叫可重复性。
lanchunhui·2016-01-10 11:00

sklearn.cluster.k_means_.k_means()

precompute_distances='auto',             n_init=10, max_iter=300, verbose=False,             tol=1e-4, random_state
py_god·2015-07-17 10:00

sklearn.cluster.k_means._kmeans_single()

kmeans_single(X, n_clusters, x_squared_norms, max_iter=300,                    init='k-means++', verbose=False, random_state
py_god·2015-07-16 21:00

sklearn.cluster.k_means_._kmeans_single()

kmeans_single(X, n_clusters, x_squared_norms, max_iter=300,                    init='k-means++', verbose=False, random_state
py_god·2015-07-16 20:00

sklearn.cluster.k_means_._k_init

random_state:随机数产生器，你懂得。。n_local_trials：每次取n_local_trials个候选中心点（第一次除外），然后从中选取最好的一个。过程
py_god·2015-07-15 14:00

sklearn.cluster.k_means_._init_centroids

random_state:用于产生中心的产生器，若为整数，则为种子，默认为globalnumpyrandomnumbergenerator。
py_god·2015-07-14 16:00
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