random_state

python sklearn模型中random_state参数的意义

random_state相当于随机数种子random.seed()。random_state与randomseed作用是相同的。
owolf·2020-02-07 07:47

sklearn-Kmeans

1fromsklearn.clusterimportKMeans2importnumpyasnp3X=np.array([[1,2],[1,4],[1,0],4[10,2],[10,4],[10,0]])5kmeans=KMeans(n_clusters=2,random_state
爽歪歪666·2020-01-09 15:00

Sklearn-train_test_split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
喵_十八·2020-01-05 15:56

监督学习(九)——核支持向量机SVM

importmglearnfromsklearn.datasetsimportmake_blobsimportmatplotlib.pyplotaspltfromsklearn.svmimportLinearSVCX,y=make_blobs(centers=4,random_state
小书同学·2019-12-31 12:36

随机种子seed（）

3.设置的seed()值仅一次有效sklearn里的DecisionTreeRegressor（）的random_state：random_state:int,RandomStateinstan
zhengda4dream·2019-12-31 03:56

kmeans的简单理解

fromsklearn.clusterimportKMeansimportnumpyasnpX=np.array([[1,2],[1,4],[1,0],[4,2],[4,4],[4,0]])kmeans=KMeans(n_clusters=2,random_state
你说你要一场·2019-12-27 00:08

sklearn学习笔记（1）--make_blobs函数及相应参数简介

sklearn.datasets.make_blobs(n_samples=100,n_features=2,centers=3,cluster_std=1.0,center_box=(-10.0,10.0),shuffle=True,random_state
明月游星空·2019-12-23 18:00

Model——

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
陆文斌·2019-12-19 19:05

机器学习中的调参

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
JSong1122·2019-12-18 05:51

Sklearn-train_test_split、random_state

sklearn.model_selection.train_test_split随机划分训练集和测试集官网文档：http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split一般形式：trai
Ten_Minutes·2019-12-08 10:21

sklearn数值特征离散值处理2: Map

importnumpyasnpimportpandasaspdpoke_df=pd.read_csv('Pokemon.csv',encoding='utf-8')poke_df.head(10)#随机抽样poke_df=poke_df.sample(random_state
弎见·2019-11-10 22:26

机器学习之随机森林实践：手写字识别、天气最高温度预测

RandomForestClassifier(n_estimators=10,criterion=’gini’,max_depth=None,bootstrap=True,random_state=
zhw864680355·2019-10-15 08:23

sklearn机器学习:逻辑回归LogisticRegression参数解析之penalty & C

sklearn.linear_model.LogisticRegression(penalty=’l2’,dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1,class_weight=None,random_state
Zen of Data Analysis·2019-10-14 14:27

sklearn机器学习：岭回归Ridge

sklearn.linear_model.Ridge(alpha=1.0,fit_intercept=True,normalize=False,copy_X=True,max_iter=None,tol=0.001,solver=’auto’,random_state
Zen of Data Analysis·2019-10-10 18:21

随手记

1.[1]所有具有随机性的Model一般都会有一个seed或是random_state参数用于控制随机种子。得到一个好的Model后，在记录参数时务必也记录下这个值，从而能够在之后重现Model。
虔诚的树·2019-09-15 18:00

深入浅出Python机器学习9——数据预处理、降维、特征提取及聚类

数据预处理使用StandScaler进行数据预处理首先手工生成一些数据：用make_blobs函数时，指定了样本数量n_samples为40，分类centers为2，随机状态random_state为50
繁华落尽心伤·2019-09-08 13:34

sklearn.linear_model.Ridge()函数解析（最清晰的解释）

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
我是管小亮 :)·2019-09-06 00:00

sklearn.ensemble.AdaBoostClassifier()函数解析（最清晰的解释）

classsklearn.ensemble.AdaBoostClassifier(base_estimator=None,n_estimators=50, learning_rate=1.0,algorithm=’SAMME.R’, random_state
我是管小亮 :)·2019-09-02 00:00

深入浅出python机器学习_9.1_数据预处理_sklearn.preprocessing.StandardScaler MinMaxScaler RobustScaler Normalizer

importnumpyimportmatplotlib.pyplotaspltfromsklearn.datasetsimportmake_blobsX,y=make_blobs(n_samples=40,centers=2,random_state
Dontla·2019-08-20 14:40

深入浅出python机器学习_7.1_支持向量机

importnumpyasnpimportmatplotlib.pyplotaspltfromsklearnimportsvmfromsklearn.datasetsimportmake_blobsX,y=make_blobs(n_samples=50,centers=2,random_state
Dontla·2019-08-14 11:11

吴裕雄--天生自然 PYTHON数据分析：糖尿病视网膜病变数据分析(续三)

=10fig=plt.figure(figsize=(25,16))forjjinrange(5):fori,(idx,row)inenumerate(df_test.sample(NUM_SAMP,random_state
吴裕雄·2019-07-24 14:00

吴裕雄--天生自然 PYTHON数据分析：糖尿病视网膜病变数据分析(续一)

(train_y.unique()):fori,(idx,row)inenumerate(df_train.loc[df_train['diagnosis']==class_id].sample(5,random_state
吴裕雄·2019-07-24 14:00

文本聚类 baseline 实例

fromsklearn.clusterimportKMeansimportnumpyasnp#hidden_dim=2X=np.array([[1,2],[1,4],[1,0],[10,2],[10,4],[10,0]])kmeans=KMeans(n_clusters=2,random_state
guotong1988·2019-07-05 09:57

数据集随机抽样

DataFrame.sample(n=None,frac=None,replace=False,weights=None,random_state=None,axis=N
kwoky·2019-07-04 11:05

神经网络③——sklearn参数介绍及应用

MLPRegressor参数和方法MLPClassifier(solver=’sgd’,activation=’relu’,alpha=1e-4,hidden_layer_sizes=(50,50),random_state
数据小斑马·2019-07-03 21:06

理解extract_patches_2d

fromsklearn.feature_extraction.imageimportextract_patches_2dextract_patches_2d(image,patch_size,max_patches=None,random_state
Einstellung·2019-06-10 23:44

决策树、随机森林、逻辑回归、SVM、XGBoost重要参数

决策树、随机森林、逻辑回归、SVM、XGBoost重要参数一、决策树二、随机森林三、逻辑回归四、SVM五、XGBoost一、决策树分类树的8个重要参数：criterion、2个随机性相关的参数（random_state
Guiabbey·2019-05-20 12:13

数据分析常用命令

读取数据：chipo=pd.read_csv(path1,sep='\t')预处理数据：查看数据有多少列:info()抽样查看：df.sample(frac=0.1,random_state=42)以itemname
snailbuster·2019-05-07 10:36

sklearn.model_selection

参数优化的方法以及模型评估拆分策略对于独立同分布(i.i.d)的数据函数名作用重要参数方法备注KFold([n_splits,shuffle,…])k折交叉验证n_splits:多少折；shuffle:是否打乱数据；random_state
Hiappsus·2019-04-15 09:42

KFold 和ShuffleSplit的区别

KFoldsklearn.model_selection.KFold(n_splits=’warn’,shuffle=False,random_state=None)ShuffleSplitsklearn.model_selection.ShuffleSplit
0点51 胜·2019-04-06 16:09

search CV

X_train_std=sc.transform(X_train)X_test_std=sc.transform(X_test)#逻辑斯蒂回归lr=LogisticRegression(C=1000.0,random_state
gavincc·2019-04-04 14:00

python机器学习 第三章（3）用sklearn训练逻辑回归模型

类以及fit方法在三种花的标准化训练集上训练模型fromsklearn.linear_modelimportLogisticRegressionlr=LogisticRegression(C=100.0,random_state
kiss_the_rain86·2019-04-01 23:43

利用sklearn中的train_test_split从原始数据中随机划分训练集和测试集

fromsklearn.model_selectionimporttrain_test_split#从原始数据中划分训练集和测试集X_tr,X_vld,lab_tr,lab_vld=train_test_split(X_train,labels_train,test_size=0.3,random_state
云端浅蓝·2019-03-19 16:40

SKlearn中分类决策树的重要参数详解

max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=None,random_state
CDA·数据分析师·2019-03-08 15:51

[机器学习]KFold 和 StratifiedKFold

ValueErrorTraceback(mostrecentcalllast)in()20print('---------分割线--------------')21sfolder=StratifiedKFold(n_splits=4,random_state
qAOOAp·2019-03-03 21:17

pandas随机抽样

DataFrame.sample(n=None,frac=None,replace=False,weights=None,random_state=None,axis=None)n是要抽取的行数。
ElsaRememberAllBug·2019-02-13 11:16

对random_state参数的理解

在学习机器学习的过程中，常常遇到random_state这个参数，下面来简单叙述一下它的作用。作用：控制随机状态。原因：为什么需要用到这样一个参数random_state（随机状态）？
枪枪枪·2019-01-23 19:06

Sklearn 中常用方法

fromsklearn.model_selectionimporttrain_test_splitX=a.iloc[:,0:-1]Y=a["label"]X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.5,random_state
ARVRinChina·2018-12-21 15:40

kmeans聚类簇个数选择

fromsklearn.clusterimportKMeansfromsklearn.metricsimportsilhouette_scoreK=range(2,20)#设置主题个数区间coef=[]forkinK:km=KMeans(n_clusters=k,random_state
KEE_HA·2018-12-04 20:07

sklearn.model_selection

model_evaluation.html一、sklearn.model_selection.KFoldK折交叉验证：sklearn.model_selection.KFold(n_splits=3,shuffle=False,random_state
csdn_lzw·2018-10-24 17:05

sklearn数据集分割函数[StratifiedShuffleSplit、split]

sklearn.model_selection.StratifiedShuffleSplit(n_splits=10,test_size=’default’,train_size=None,random_state
墨氲·2018-09-22 23:05

使用sklearn预测波士顿房价

fromsklearn.cross_validationimporttrain_test_splitimportnumpyasnpx_train,x_test,y_train,y_test=train_test_split(x,y,random_state
qq_33361080·2018-09-11 10:01

sklearn.linear_model.LogisticRegression 参数

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
ZoeLan_·2018-08-09 13:59

【Python第三方包】scikit-learn

KFold与StratifiedKFold的区别classsklearn.model_selection.StratifiedKFold(n_splits=3,shuffle=False,random_state
dominic_z·2018-08-02 16:23

sklearn.model_selection.ParameterSampler 使用给定分布生成参数，进行参数搜索

classUniformDistribution(object):def__init__(self,low,high):assertlow<=highself.low=lowself.high=highdefrvs(self,size=None,random_state
青盏·2018-07-29 16:17

（八）sklearn神经网络

lbfgs’,‘sgd’,‘adam’},default‘adam’clf=MLPClassifier(solver='lbfgs',alpha=1e-5,hidden_layer_sizes=(5,2),random_state
HawardScut·2018-07-29 14:24

sklearn的train_test_split的random_state

我们在使用sklearn的train_test_split函数随机划分数据集生成trainingset于testset时，在函数train_test_split中有一个参数为random_state。
QiujieDong·2018-07-22 10:19

train_test_split函数

X_train,X_test,y_train,y_test=cross_validation.train_test_split(train_data,train_target,test_size=0.2,random_state
庆志的小徒弟·2018-07-19 09:19

机器学习：模型评估与sklearn实现(二)_交叉验证

二、KFold1.原型sklearn.model_selection.KFold(n_splits=3,shuffle=False,random_state=None)2.参数
BQW_·2018-07-12 01:57

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
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