ML之SVM:利用SVM算法(超参数组合进行单线程网格搜索+3fCrVa)对20类新闻文本数据集进行分类预测、评估

简介: ML之SVM:利用SVM算法(超参数组合进行单线程网格搜索+3fCrVa)对20类新闻文本数据集进行分类预测、评估

输出结果

Fitting 3 folds for each of 12 candidates, totalling 36 fits

[CV] svc__C=0.1, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=0.01 -   6.2s

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.2s remaining:    0.0s

[CV] svc__C=0.1, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=0.01 -   7.1s

[CV] svc__C=0.1, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=0.01 -   7.0s

[CV] svc__C=0.1, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=0.1 -   6.9s

[CV] svc__C=0.1, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=0.1 -   6.8s

[CV] svc__C=0.1, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=0.1 -   6.3s

[CV] svc__C=0.1, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=1.0 -   6.3s

[CV] svc__C=0.1, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=1.0 -   7.0s

[CV] svc__C=0.1, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=0.1, svc__gamma=1.0 -   8.1s

[CV] svc__C=0.1, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=10.0 -   8.8s

[CV] svc__C=0.1, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=10.0 -  10.7s

[CV] svc__C=0.1, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=0.1, svc__gamma=10.0 -   9.4s

[CV] svc__C=1.0, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=0.01 -   8.4s

[CV] svc__C=1.0, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=0.01 -   6.7s

[CV] svc__C=1.0, svc__gamma=0.01 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=0.01 -   6.9s

[CV] svc__C=1.0, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=0.1 -   6.6s

[CV] svc__C=1.0, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=0.1 -   6.2s

[CV] svc__C=1.0, svc__gamma=0.1 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=0.1 -   6.8s

[CV] svc__C=1.0, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=1.0 -   7.6s

[CV] svc__C=1.0, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=1.0 -   7.7s

[CV] svc__C=1.0, svc__gamma=1.0 ......................................

[CV] ............................. svc__C=1.0, svc__gamma=1.0 -   8.2s

[CV] svc__C=1.0, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=10.0 -   6.7s

[CV] svc__C=1.0, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=10.0 -   8.4s

[CV] svc__C=1.0, svc__gamma=10.0 .....................................

[CV] ............................ svc__C=1.0, svc__gamma=10.0 -   9.5s

[CV] svc__C=10.0, svc__gamma=0.01 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=0.01 -  10.1s

[CV] svc__C=10.0, svc__gamma=0.01 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=0.01 -   9.9s

[CV] svc__C=10.0, svc__gamma=0.01 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=0.01 -   8.8s

[CV] svc__C=10.0, svc__gamma=0.1 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=0.1 -   9.2s

[CV] svc__C=10.0, svc__gamma=0.1 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=0.1 -   7.7s

[CV] svc__C=10.0, svc__gamma=0.1 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=0.1 -   6.9s

[CV] svc__C=10.0, svc__gamma=1.0 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=1.0 -   8.0s

[CV] svc__C=10.0, svc__gamma=1.0 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=1.0 -   9.5s

[CV] svc__C=10.0, svc__gamma=1.0 .....................................

[CV] ............................ svc__C=10.0, svc__gamma=1.0 -   9.0s

[CV] svc__C=10.0, svc__gamma=10.0 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=10.0 -   8.6s

[CV] svc__C=10.0, svc__gamma=10.0 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=10.0 -   8.1s

[CV] svc__C=10.0, svc__gamma=10.0 ....................................

[CV] ........................... svc__C=10.0, svc__gamma=10.0 -   9.0s

[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:  4.8min finished

单线程:输出最佳模型在测试集上的准确性: 0.8226666666666667


设计思

image.png

 

核心代

class GridSearchCV(BaseSearchCV):

   """Exhaustive search over specified parameter values for an estimator.

 

   .. deprecated:: 0.18

   This module will be removed in 0.20.

   Use :class:`sklearn.model_selection.GridSearchCV` instead.

 

   Important members are fit, predict.

 

   GridSearchCV implements a "fit" and a "score" method.

   It also implements "predict", "predict_proba", "decision_function",

   "transform" and "inverse_transform" if they are implemented in the

   estimator used.

 

   The parameters of the estimator used to apply these methods are

    optimized

   by cross-validated grid-search over a parameter grid.

 

   Read more in the :ref:`User Guide <grid_search>`.

 

   Parameters

   ----------

   estimator : estimator object.

   A object of that type is instantiated for each grid point.

   This is assumed to implement the scikit-learn estimator interface.

   Either estimator needs to provide a ``score`` function,

   or ``scoring`` must be passed.

 

   param_grid : dict or list of dictionaries

   Dictionary with parameters names (string) as keys and lists of

   parameter settings to try as values, or a list of such

   dictionaries, in which case the grids spanned by each dictionary

   in the list are explored. This enables searching over any sequence

   of parameter settings.

 

   scoring : string, callable or None, default=None

   A string (see model evaluation documentation) or

   a scorer callable object / function with signature

   ``scorer(estimator, X, y)``.

   If ``None``, the ``score`` method of the estimator is used.

 

   fit_params : dict, optional

   Parameters to pass to the fit method.

 

   n_jobs: int, default: 1 :

   The maximum number of estimators fit in parallel.

 

   - If -1 all CPUs are used.

 

   - If 1 is given, no parallel computing code is used at all,

   which is useful for debugging.

 

   - For ``n_jobs`` below -1, ``(n_cpus + n_jobs + 1)`` are used.

   For example, with ``n_jobs = -2`` all CPUs but one are used.

 

   .. versionchanged:: 0.17

   Upgraded to joblib 0.9.3.

 

   pre_dispatch : int, or string, optional

   Controls the number of jobs that get dispatched during parallel

   execution. Reducing this number can be useful to avoid an

   explosion of memory consumption when more jobs get dispatched

   than CPUs can process. This parameter can be:

 

   - None, in which case all the jobs are immediately

   created and spawned. Use this for lightweight and

   fast-running jobs, to avoid delays due to on-demand

   spawning of the jobs

 

   - An int, giving the exact number of total jobs that are

   spawned

 

   - A string, giving an expression as a function of n_jobs,

   as in '2*n_jobs'

 

   iid : boolean, default=True

   If True, the data is assumed to be identically distributed across

   the folds, and the loss minimized is the total loss per sample,

   and not the mean loss across the folds.

 

   cv : int, cross-validation generator or an iterable, optional

   Determines the cross-validation splitting strategy.

   Possible inputs for cv are:

 

   - None, to use the default 3-fold cross-validation,

   - integer, to specify the number of folds.

   - An object to be used as a cross-validation generator.

   - An iterable yielding train/test splits.

 

   For integer/None inputs, if the estimator is a classifier and ``y`` is

   either binary or multiclass,

   :class:`sklearn.model_selection.StratifiedKFold` is used. In all

   other cases, :class:`sklearn.model_selection.KFold` is used.

 

   Refer :ref:`User Guide <cross_validation>` for the various

   cross-validation strategies that can be used here.

 

   refit : boolean, default=True

   Refit the best estimator with the entire dataset.

   If "False", it is impossible to make predictions using

   this GridSearchCV instance after fitting.

 

   verbose : integer

   Controls the verbosity: the higher, the more messages.

 

   error_score : 'raise' (default) or numeric

   Value to assign to the score if an error occurs in estimator fitting.

   If set to 'raise', the error is raised. If a numeric value is given,

   FitFailedWarning is raised. This parameter does not affect the refit

   step, which will always raise the error.

 

 

   Examples

   --------

   >>> from sklearn import svm, grid_search, datasets

   >>> iris = datasets.load_iris()

   >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}

   >>> svr = svm.SVC()

   >>> clf = grid_search.GridSearchCV(svr, parameters)

   >>> clf.fit(iris.data, iris.target)

   ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS

   GridSearchCV(cv=None, error_score=...,

   estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,

   decision_function_shape='ovr', degree=..., gamma=...,

   kernel='rbf', max_iter=-1, probability=False,

   random_state=None, shrinking=True, tol=...,

   verbose=False),

   fit_params={}, iid=..., n_jobs=1,

   param_grid=..., pre_dispatch=..., refit=...,

   scoring=..., verbose=...)

 

 

   Attributes

   ----------

   grid_scores_ : list of named tuples

   Contains scores for all parameter combinations in param_grid.

   Each entry corresponds to one parameter setting.

   Each named tuple has the attributes:

 

   * ``parameters``, a dict of parameter settings

   * ``mean_validation_score``, the mean score over the

   cross-validation folds

   * ``cv_validation_scores``, the list of scores for each fold

 

   best_estimator_ : estimator

   Estimator that was chosen by the search, i.e. estimator

   which gave highest score (or smallest loss if specified)

   on the left out data. Not available if refit=False.

 

   best_score_ : float

   Score of best_estimator on the left out data.

 

   best_params_ : dict

   Parameter setting that gave the best results on the hold out data.

 

   scorer_ : function

   Scorer function used on the held out data to choose the best

   parameters for the model.

 

   Notes

   ------

   The parameters selected are those that maximize the score of the left

    out

   data, unless an explicit score is passed in which case it is used instead.

 

   If `n_jobs` was set to a value higher than one, the data is copied for

    each

   point in the grid (and not `n_jobs` times). This is done for efficiency

   reasons if individual jobs take very little time, but may raise errors if

   the dataset is large and not enough memory is available.  A

    workaround in

   this case is to set `pre_dispatch`. Then, the memory is copied only

   `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *

   n_jobs`.

 

   See Also

   ---------

   :class:`ParameterGrid`:

   generates all the combinations of a hyperparameter grid.

 

   :func:`sklearn.cross_validation.train_test_split`:

   utility function to split the data into a development set usable

   for fitting a GridSearchCV instance and an evaluation set for

   its final evaluation.

 

   :func:`sklearn.metrics.make_scorer`:

   Make a scorer from a performance metric or loss function.

 

   """

   def __init__(self, estimator, param_grid, scoring=None,

    fit_params=None,

       n_jobs=1, iid=True, refit=True, cv=None, verbose=0,

       pre_dispatch='2*n_jobs', error_score='raise'):

       super(GridSearchCV, self).__init__(estimator, scoring, fit_params,

        n_jobs, iid, refit, cv, verbose, pre_dispatch, error_score)

       self.param_grid = param_grid

       _check_param_grid(param_grid)

 

   def fit(self, X, y=None):

       """Run fit with all sets of parameters.

       Parameters

       ----------

       X : array-like, shape = [n_samples, n_features]

           Training vector, where n_samples is the number of samples and

           n_features is the number of features.

       y : array-like, shape = [n_samples] or [n_samples, n_output],

        optional

           Target relative to X for classification or regression;

           None for unsupervised learning.

       """

       return self._fit(X, y, ParameterGrid(self.param_grid))


目录
打赏
0
0
0
0
1044
分享
相关文章
K-means聚类算法是机器学习中常用的一种聚类方法,通过将数据集划分为K个簇来简化数据结构
K-means聚类算法是机器学习中常用的一种聚类方法,通过将数据集划分为K个簇来简化数据结构。本文介绍了K-means算法的基本原理,包括初始化、数据点分配与簇中心更新等步骤,以及如何在Python中实现该算法,最后讨论了其优缺点及应用场景。
312 6
利用SVM(支持向量机)分类算法对鸢尾花数据集进行分类
本文介绍了如何使用支持向量机(SVM)算法对鸢尾花数据集进行分类。作者通过Python的sklearn库加载数据,并利用pandas、matplotlib等工具进行数据分析和可视化。
285 70
基于WOA算法的SVDD参数寻优matlab仿真
该程序利用鲸鱼优化算法(WOA)对支持向量数据描述(SVDD)模型的参数进行优化,以提高数据分类的准确性。通过MATLAB2022A实现,展示了不同信噪比(SNR)下模型的分类误差。WOA通过模拟鲸鱼捕食行为,动态调整SVDD参数,如惩罚因子C和核函数参数γ,以寻找最优参数组合,增强模型的鲁棒性和泛化能力。
158 31
基于GA遗传算法的PID控制器参数优化matlab建模与仿真
本项目基于遗传算法(GA)优化PID控制器参数,通过空间状态方程构建控制对象,自定义GA的选择、交叉、变异过程,以提高PID控制性能。与使用通用GA工具箱相比,此方法更灵活、针对性强。MATLAB2022A环境下测试,展示了GA优化前后PID控制效果的显著差异。核心代码实现了遗传算法的迭代优化过程,最终通过适应度函数评估并选择了最优PID参数,显著提升了系统响应速度和稳定性。
454 15
基于大爆炸优化算法的PID控制器参数寻优matlab仿真
本研究基于大爆炸优化算法对PID控制器参数进行寻优,并通过Matlab仿真对比优化前后PID控制效果。使用MATLAB2022a实现核心程序,展示了算法迭代过程及最优PID参数的求解。大爆炸优化算法通过模拟宇宙大爆炸和大收缩过程,在搜索空间中迭代寻找全局最优解,特别适用于PID参数优化,提升控制系统性能。
【新闻文本分类识别系统】Python+卷积神经网络算法+人工智能+深度学习+计算机毕设项目+Django网页界面平台
文本分类识别系统。本系统使用Python作为主要开发语言,首先收集了10种中文文本数据集("体育类", "财经类", "房产类", "家居类", "教育类", "科技类", "时尚类", "时政类", "游戏类", "娱乐类"),然后基于TensorFlow搭建CNN卷积神经网络算法模型。通过对数据集进行多轮迭代训练,最后得到一个识别精度较高的模型,并保存为本地的h5格式。然后使用Django开发Web网页端操作界面,实现用户上传一段文本识别其所属的类别。
242 1
【新闻文本分类识别系统】Python+卷积神经网络算法+人工智能+深度学习+计算机毕设项目+Django网页界面平台
基于禁忌搜索算法的VRP问题求解matlab仿真,带GUI界面,可设置参数
该程序基于禁忌搜索算法求解车辆路径问题(VRP),使用MATLAB2022a版本实现,并带有GUI界面。用户可通过界面设置参数并查看结果。禁忌搜索算法通过迭代改进当前解,并利用记忆机制避免陷入局部最优。程序包含初始化、定义邻域结构、设置禁忌列表等步骤,最终输出最优路径和相关数据图表。
基于最小二乘递推算法的系统参数辨识matlab仿真
该程序基于最小二乘递推(RLS)算法实现系统参数辨识,对参数a1、b1、a2、b2进行估计并计算误差及收敛曲线,对比不同信噪比下的估计误差。在MATLAB 2022a环境下运行,结果显示了四组误差曲线。RLS算法适用于实时、连续数据流中的动态参数辨识,通过递推方式快速调整参数估计,保持较低计算复杂度。
基于极大似然算法的系统参数辨识matlab仿真
本程序基于极大似然算法实现系统参数辨识,对参数a1、b1、a2、b2进行估计,并计算估计误差及收敛曲线,对比不同信噪比下的误差表现。在MATLAB2022a版本中运行,展示了参数估计值及其误差曲线。极大似然估计方法通过最大化观测数据的似然函数来估计未知参数,适用于多种系统模型。
支付宝商业化广告算法问题之基于pretrain—>finetune范式的知识迁移中,finetune阶段全参数训练与部分参数训练的效果如何比较
支付宝商业化广告算法问题之基于pretrain—>finetune范式的知识迁移中,finetune阶段全参数训练与部分参数训练的效果如何比较
下一篇
oss创建bucket
目录
AI助理

你好,我是AI助理

可以解答问题、推荐解决方案等