n_jobs

机器学习sklearn中常见的线性模型参数释义

fromsklearn.linear_modelimportLinearRegressionLinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
随遇而安_小强·2020-08-02 20:09

sklearn常用机器学习算法参数详解

fromsklearn.linear_modelimportLinearRegressionLinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
Eric_Squirrel·2020-07-29 07:09

Python 中jupyternotebook中%%time使用报错

出错代码#n_jobs=3，表示只使用3个内核进行计算%%timebagging_clf1=BaggingClassifier(DecisionTreeClassifier(random_state=666
新人王小五·2020-07-27 10:00

from common.utils import plot_learning_curve ModuleNotFoundError: No module named 'common'

使用以下函数（函数来源官网）：defplot_learning_curve(estimator,title,X,y,ylim=None,cv=None,n_jobs=1,train_sizes=np.linspace
我爱棒棒糖·2020-07-16 05:04

scikit-learn 分类 KNeighborsClassifier

n_neighbors=5,weights=’uniform’,algorithm=’auto’,leaf_size=30,p=2,metric=’minkowski’,metric_params=None,n_jobs
Tianweidadada·2020-07-15 19:15

sklearn.cross_val_score和sklearn.roc_auc_score

1、sklearn.model_selection.cross_val_score(estimator,X,y=None,groups=None,scoring=None,cv=None,n_jobs=
gyl2016·2020-07-15 18:33

机器学习之最近邻（KNN）实践：鸢尾花分类

n_neighbors=5,weights='uniform',algorithm='',leaf_size='30',p=2,metric='minkowski',metric_params=None,n_jobs
zhw864680355·2020-07-15 12:18

【sklearn机器学习】KNN分类

n_neighbors=5,weights=’uniform’,algorithm=’auto’,leaf_size=30,p=2,metric=’minkowski’,metric_params=None,n_jobs
YFR718·2020-07-14 06:52

【sklearn】使用GridSearchCV查找最优参数

：classGridSearchCV(BaseSearchCV):def__init__(self,estimator,param_grid,scoring=None,fit_params=None,n_jobs
Tuzi_bo·2020-07-13 14:52

python 机器学习 sklearn 广义线性模型

广义的线性模型是最最常用和我个人认为最重要的最小二乘classsklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
水野与小太郎·2020-07-11 17:51

sklearn.metrics.pairwise_distances

sklearn.metrics.pairwise_distancessklearn.metrics.pairwise_distances(X,Y=None,metric=’euclidean’,n_jobs
lonelykid96·2020-07-11 11:28

python——cross_val_score()函数、ShuffleSplit()函数、zip()函数

cross_val_score():#cross_val_score(estimator,X,y=None,scoring=None,cv=None,n_jobs=1)#该函数用交叉检验(cross-validation
watermelon12138·2020-07-08 12:31

2018.2.13

2018.2.13#DBSCANbeginsdb=cluster.DBSCAN(eps=0.1011,min_samples=115,n_jobs=-1)db.fit(first_set)r=pd.concat
swy_swy_swy·2020-07-08 07:02

sklearn中的cross_val_score()函数参数

sklearn.cross_validation.cross_val_score(estimator,X,y=None,scoring=None,cv=None,n_jobs=1,verbose=0,fit_params
往往·2020-07-08 02:28

python-KNN分类（1）：调用KNeighborsClassifier()实现

            algorithm='auto',leaf_size=30,            p=2,metric='minkowski',            metric_params=None,n_jobs
JoannaJuanCV·2020-06-30 13:42

sklearn交叉验证cross_val_score参数解析

fromsklearn.model_selectionimportcross_val_scorecross_val_score(estimator,X,y=None,groups=None,scoring=None,cv=None,verbose=0,fit_params=None,pre_dispatch='2*n_jobs
wzd_AI·2020-06-29 04:22

【KNN】sklearn.neighbors.KNeighborsClassifier的参数说明

n_neighbors=5,weights=’uniform’,algorithm=’auto’,leaf_size=30,p=2,metric=’minkowski’,metric_params=None,n_jobs
suu_fxhong·2020-06-29 02:37

利用sklearn在训练模型时进行参数调优的方法

sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, 
学不会coding的程序员·2020-06-29 00:08

API详解:sklearn.linear_model.LinearRegression

，先定义一个线性回归对象lr=sklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
Sehr_Gut·2020-06-28 22:13

超参数调节

GridSearchCV类classsklearn.model_selection.GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs
蜘蛛侠不会飞·2020-06-25 18:20

estimate_bandwidth

sklearn.cluster.estimate_bandwidth(X,quantile=0.3,n_samples=None,random_state=0,n_jobs=1)字面意思：预估带宽Estimatethebandwidthtousewiththemean-shiftalgorithm
你说你要一场·2020-06-25 17:16

Python多进程实现并行化随机森林

单进程训练函数生成数据集模块——生成部分数据集单进程训练函数代码3.2并行化预测3.2.1预测函数3.2.2单进程预测函数4.并行化结果分析5.源码参考资料1.前言Python其实已经实现过随机森林，而且有并行化的参数n_jobs
有问题先搜报错~·2020-06-24 23:10

Kmeans聚类K值的确定

通过手肘法确定Kmeans聚类的最优K值SSE=[]#存放每次结果的误差平方和forkinrange(5,50):estimator=KMeans(n_clusters=k,max_iter=100,n_jobs
暴躁的猴子·2020-06-24 18:12

sklearn.linear_model.LinearRegression

最小二乘法线性回归：sklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
每天进步一点点2017·2020-06-24 00:40

GridSearchCV参数

fromsklearn.model_selectionimportGridSearchCV2.函数原型classsklearn.model_selection.GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs
Kyrie_Irving·2020-06-21 23:10

python 检视过拟合之validation_curve

不同取值的算法的得分通过这种曲线可以更加直观看出改变模型中的参数，有没有出现过拟合validation_curve(estimator,X,y,param_name,param_range,groups=None,cv=None,scoring=None,n_jobs
思想流浪者·2020-06-21 04:21

Kmeans

n_init=10,max_iter=300,tol=0.0001,precompute_distances='auto',verbose=0,random_state=None,copy_x=True,n_jobs
似海深蓝·2020-04-28 16:59

Parallel for loop

frommathimportsqrtfromjoblibimportParallel,delayed#single-corecodesqroots_1=[sqrt(i**2)foriinrange(10)]#parallelcodesqroots_2=Parallel(n_jobs
勤奋的红狐狸·2020-04-09 00:09

多分类的logisticRegression和SFS

class_weight='balanced',solver='lbfgs')sfs1=SFS(logi,k_features=80,forward=True,floating=False,verbose=2,cv=5,n_jobs
美环花子若野·2020-04-05 15:31

sklearn的GridSearchCV

classsklearn.model_selection.GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs=1,
小幸运Q·2020-02-02 03:10

sklearn.neighbors.KNeighborsClassifier(k近邻分类器)

n_neighbors=5,weights='uniform',algorithm='auto',leaf_size=30,p=2,metric='minkowski',metric_params=None,n_jobs
明月游星空·2019-12-27 13:00

sklearn.neighbors.KNeighborsClassifier(k近邻分类器)

n_neighbors=5,weights='uniform',algorithm='auto',leaf_size=30,p=2,metric='minkowski',metric_params=None,n_jobs
明月游星空·2019-12-27 13:00

scikit_learn (sklearn)库中NearestNeighbors（最近邻）函数的各参数说明

NearestNeighbors(n_neighbors=5,radius=1.0,algorithm='auto',leaf_size=30,metric='minkowski',p=2,metric_params=None,n_jobs
明月游星空·2019-12-26 18:00

通俗得说线性回归算法（二）线性回归实战

defLinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=None)-fit_intercept：默认为true
zzzzMing·2019-10-15 18:00

XGBoost、LightGBM参数讲解及实战

xgboost.XGBClassifier1.1参数1.1.1通用参数：booster=‘gbtree’使用的提升数的种类gbtree,gblinearordartsilent=True:训练过程中是否打印日志n_jobs
tan_2810·2019-09-25 18:00

sklearn--NearestNeighbors（监督学习）

self,n_neighbors=5,radius=1.0,algorithm='auto',leaf_size=30,metric='minkowski',p=2,metric_params=None,n_jobs
萧忆情Alex丶·2019-08-17 13:47

sklearn--NearestNeighbors（监督学习）

self,n_neighbors=5,radius=1.0,algorithm='auto',leaf_size=30,metric='minkowski',p=2,metric_params=None,n_jobs
萧忆情Alex丶·2019-08-17 13:47

python基础之learning_curve（学习曲线）

train_sizes=array([0.1,0.325,0.55,0.775,1.]),cv=None,scoring=None,exploit_incremental_learning=False,n_jobs
勇于自信·2019-08-02 16:39

cross_val_score 交叉验证与 K折交叉验证，嗯都是抄来的，自己作个参考

也可以用来选择特征sklearn.model_selection.cross_val_score(estimator,X,y=None,groups=None,scoring=None,cv=None,n_jobs
东西·2019-05-24 23:00

【Python】sklearn中的cross_val_score()函数参数

sklearn.cross_validation.cross_val_score(estimator,X,y=None,scoring=None,cv=None,n_jobs=1,verbose=0,fit_params
Asher117·2019-02-18 14:11

Isolation Forest孤立森林（二）之sklearn实现,源码分析

classsklearn.ensemble.IsolationForest(n_estimators=100,max_samples=’auto’,contamination=’legacy’,max_features=1.0,bootstrap=False,n_jobs
纽扣子·2019-01-02 11:20

sklearn中一些参数

普通最小二乘法classsklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs
__gyl__·2018-12-09 21:40

sklearn包中cross_val_score进行交叉验证

sklearn.cross_validation.cross_val_score(estimator,X,y=None,scoring=None,cv=None,n_jobs=1,verbose=0,fit_params
ckSpark·2018-11-24 00:46

sklearn.model_selection.cross_val_score

sklearn.model_selection.cross_val_score(estimator,X,y=None,groups=None,scoring=None,cv=’warn’,n_jobs=
SilenceHell·2018-11-21 18:51

sklearn.model_selection.cross_val_score

sklearn.model_selection.cross_val_score(estimator,X,y=None,groups=None,scoring=None,cv=’warn’,n_jobs=
SilenceHell·2018-11-21 18:51

线型回归模型LinearRegression

classsklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=None
SilenceHell·2018-11-21 16:12

线型回归模型LinearRegression

classsklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=None
SilenceHell·2018-11-21 16:12

mac 下sklearn grid_search无法启动多线程

如指定n_Jobs为-1（最大），或>1时，无法执行且无任何输出。
来个芒果·2018-11-10 19:45

算法实战 一.线性回归

sklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=None)参数说明
ShannonGu·2018-10-23 16:52

算法实践1_线性回归

参数解释sklearn.linear_model.LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=None)
Datawhale·2018-10-06 20:19
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