scikit-learn:3.2. Grid Search: Searching for estimator parameters
參考:http://scikit-learn.org/stable/modules/grid_search.html
GridSearchCV通过(蛮力)搜索參数空间(參数的全部可能组合)。寻找最好的 Cross-validation:
evaluating estimator performance score相应的超參数(翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47099275)。比如Support
Vector Classifier的 C, kernel and gamma ,Lasso的alpha。etc。
A search consists of:
- an estimator (regressor or classifier such as sklearn.svm.SVC());
- a parameter space;
- a method for searching or sampling candidates;
- a cross-validation scheme
- a score
function.
RandomizedSearchCV 通过一定的分布sample候选參数。而不是搜索全部參数组合。
本节我们介绍 GridSearchCV、RandomizedSearchCV 、以及parameter
search的小Tips,最后介绍蛮力搜索的alternatives。
1、Exhaustive
Grid Search
GridSearchCV的參数param_grid定义搜索网格。
两个样例说明一切:
- See Parameter
estimation using grid search with cross-validation for an example of Grid Search computation on the digits dataset. - See Sample
pipeline for text feature extraction and evaluation for an example of Grid Search coupling parameters from a text documents feature extractor (n-gram count vectorizer and TF-IDF transformer) with a classifier (here a linear SVM trained with SGD with
either elastic net or L2 penalty) using a pipeline.Pipeline instance.
2、Randomized
Parameter Optimization
RandomizedSearchCV 通过在參数可能的取值的某个分布中sample一组參数。优点是:能够设定独立于參数(及全部取值)详细数量的一个搜索次数;加入无效的參数也不会减少效率。
搜索的次数通过 n_iter 设定,对于每个參数,假设是连续的取值。则通过一定的分布sample,假设是离散的取值,则通过uniform分布sample,比如:
[{'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
'kernel': ['rbf'], 'class_weight':['auto', None]}]
scipy.stats module提供了非常多用来sample參数的distributions,如expon, gamma, uniform or randint.
对于连续的參数,如 C ,一定要选择连续的分布来sample,而且适当增大 n_iter 通常会搜索到更好的參数组合。
给个样例:
- Comparing
randomized search and grid search for hyperparameter estimation compares the usage and efficiency of randomized search and grid search.
3、Tips
for parameter search(这几个建议很靠谱。。。
)
1)详细化目标函数
參数搜索默认使用score function(
即,分类用sklearn.metrics.accuracy_score 回归用sklearn.metrics.r2_score )来衡量參数的好坏对于有些应用(比方分类unbalance,score不是非常好的标准),通过详细化GridSearchCV和RandomizedSearchCV 的scoring parameter。See The
scoring parameter: defining model evaluation rules for more details.
2)综合estimators和parameter sapces(同一时候考虑预測器和參数空间)
Pipeline:
chaining estimators describes building composite estimators whose parameter space can be searched with these tools.
3)模型选择:先训练、再评估
用训练集选择模型。用測试集验证模型(using
the cross_validation.train_test_split utility
function.)(it is recommended to split the data into a development set (to be
fed to the GridSearchCV instance)
and an evaluation set to compute performance metrics.)
4)并行搜索
n_jobs=-1.
自己主动使用全部核。
5)robustness to failure(增强搜索错误的鲁棒性)
有些參数组合对于某些folds
of the data会failure,进而导致整个search failure,虽然其它的參数组合没有问题。
设定 error_score=0 (or =np.NaN)
能够使search过程忽略这种failure,只抛出一个warning,并将这种search结果设为0 (or =np.NaN)
,可以提高搜索遇到错误时的鲁棒性!
4、Alternatives
to brute force parameter search(没太看懂,还是不翻译了)
3.2.4.1. Model specific cross-validation
Some models can fit data for a range of value of some parameter almost as efficiently as fitting the estimator for a single value of the parameter. This feature can be leveraged to perform
a more efficient cross-validation used for model selection of this parameter.
The most common parameter amenable to this strategy is the parameter encoding the strength of the regularizer. In this case we say that we compute theregularization path of
the estimator.
Here is the list of such models:
linear_model.ElasticNetCV([l1_ratio, eps, ...]) | Elastic Net model with iterative fitting along a regularization path |
linear_model.LarsCV([fit_intercept, ...]) | Cross-validated Least Angle Regression model |
linear_model.LassoCV([eps, n_alphas, ...]) | Lasso linear model with iterative fitting along a regularization path |
linear_model.LassoLarsCV([fit_intercept, ...]) | Cross-validated Lasso, using the LARS algorithm |
linear_model.LogisticRegressionCV([Cs, ...]) | Logistic Regression CV (aka logit, MaxEnt) classifier. |
linear_model.MultiTaskElasticNetCV([...]) | Multi-task L1/L2 ElasticNet with built-in cross-validation. |
linear_model.MultiTaskLassoCV([eps, ...]) | Multi-task L1/L2 Lasso with built-in cross-validation. |
linear_model.OrthogonalMatchingPursuitCV([...]) | Cross-validated Orthogonal Matching Pursuit model (OMP) |
linear_model.RidgeCV([alphas, ...]) | Ridge regression with built-in cross-validation. |
linear_model.RidgeClassifierCV([alphas, ...]) | Ridge classifier with built-in cross-validation. |
3.2.4.2. Information Criterion
Some models can offer an information-theoretic closed-form formula of the optimal estimate of the regularization parameter by computing a single regularization path (instead of several when
using cross-validation).
Here is the list of models benefitting from the Aikike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for automated model selection:
linear_model.LassoLarsIC([criterion, ...]) | Lasso model fit with Lars using BIC or AIC for model selection |
3.2.4.3. Out of Bag Estimates
When using ensemble methods base upon bagging, i.e. generating new training sets using sampling with replacement, part of the training set remains unused. For each classifier in the ensemble,
a different part of the training set is left out.
This left out portion can be used to estimate the generalization error without having to rely on a separate validation set. This estimate comes “for free” as no additional data is needed and
can be used for model selection.
This is currently implemented in the following classes:
ensemble.RandomForestClassifier([...]) | A random forest classifier. |
ensemble.RandomForestRegressor([...]) | A random forest regressor. |
ensemble.ExtraTreesClassifier([...]) | An extra-trees classifier. |
ensemble.ExtraTreesRegressor([n_estimators, ...]) | An extra-trees regressor. |
ensemble.GradientBoostingClassifier([loss, ...]) | Gradient Boosting for classification. |
ensemble.GradientBoostingRegressor([loss, ...]) | Gradient Boosting for regression. |
scikit-learn:3.2. Grid Search: Searching for estimator parameters的更多相关文章
- 3.2. Grid Search: Searching for estimator parameters
3.2. Grid Search: Searching for estimator parameters Parameters that are not directly learnt within ...
- How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras
Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are n ...
- scikit learn 模块 调参 pipeline+girdsearch 数据举例:文档分类 (python代码)
scikit learn 模块 调参 pipeline+girdsearch 数据举例:文档分类数据集 fetch_20newsgroups #-*- coding: UTF-8 -*- import ...
- (原创)(四)机器学习笔记之Scikit Learn的Logistic回归初探
目录 5.3 使用LogisticRegressionCV进行正则化的 Logistic Regression 参数调优 一.Scikit Learn中有关logistics回归函数的介绍 1. 交叉 ...
- Grid search in the tidyverse
@drsimonj here to share a tidyverse method of grid search for optimizing a model's hyperparameters. ...
- (原创)(三)机器学习笔记之Scikit Learn的线性回归模型初探
一.Scikit Learn中使用estimator三部曲 1. 构造estimator 2. 训练模型:fit 3. 利用模型进行预测:predict 二.模型评价 模型训练好后,度量模型拟合效果的 ...
- Extjs4.2 Grid搜索Ext.ux.grid.feature.Searching的使用
背景 Extjs4.2 默认提供的Search搜索,功能还是非常强大的,只是对于国内的用户来说,还是不习惯在每列里面单击好几下再筛选,于是相当当初2.2里面的搜索,更加的实用点,于是在4.2里面实现. ...
- Ext.ux.grid.feature.Searching 解析查询参数,动态产生linq lambda表达式
上篇文章中http://www.cnblogs.com/qidian10/p/3209439.html我们介绍了如何使用Grid的查询组建,而且将查询的参数传递到了后台. 那么我们后台如何介绍参数,并 ...
- Grid Search学习
转自:https://www.cnblogs.com/ysugyl/p/8711205.html Grid Search:一种调参手段:穷举搜索:在所有候选的参数选择中,通过循环遍历,尝试每一种可能性 ...
随机推荐
- ajaxFileUpload 返回的数据报错
$.ajaxFileUpload({ url : '/updateMallGoods', data : { "goodsName":goodsName, "proDesc ...
- Linux学习之计算机基础理论
一.描述计算机的组成及其功能. 计算机系统是由硬件系统(hardware)和软件系统(software system)两部分组成. 硬件系统: 从硬件基本结构上来讲,计算机是由运算器.控制器.存储器. ...
- pip 出错
pip 升级到10以上出错 ImportError: cannot import name 'main' 解决方法一: 降低pip的版本号 python -m pip install pip==9.0 ...
- scrapy爬取boss直聘实习生数据
这个..是我最近想找实习单位..结果发现boss上很多实习单位名字就叫‘实习生’.......太不讲究了 == 难怪一直搜不到..咳,其实是我自己水平有限,有些简历根本就投不出去 == 所以就想爬下b ...
- 继续过Hard题目.周五
# Title Editorial Acceptance Difficulty Frequency . 65 Valid Number 12.6% Hard . 126 Word ...
- cocos2d_android 第一个游戏
依据上一篇文章.创建好cocos2d--android的开发环境 先上效果图 实现该效果的代码: package com.cn.firstgame; import org.cocos2d.layers ...
- 解决The hierarchy of the type is inconsistent错误
可能的原因:自己的类继承于某个类,这个类或者这个类继承的类或者再往上继承的某个类所在的jar包没有被引入. 比如:使用Spring的AOP时,假设须要继承MethodBeforeAdvice和Afte ...
- 想做web前端project师应该学习些什么?
偶然间看到这篇文章.感觉博主写的挺不错的,假设你想做web前端project师的话,建议您阅读下面这篇文章,事实上web前端project师所做的工作事实上就是站点设计,有些小公司的美工事实上就是做w ...
- bzoj1029: [JSOI2007]建筑抢修(堆+贪心)
1029: [JSOI2007]建筑抢修 题目:传送门 题解: 一道以前就做过的水题(找个水题签个到嘛...) 很明显就是一道贪心题,这里我们用一个堆来维护 具体看代码吧,很容易YY所以不讲 代码: ...
- wireshark界面调整成英文的
https://ask.wireshark.org/questions/48823/change-the-gui-language 英文版设置 From the Edit (Bearbeiten) m ...