在 GridSearchCV 中使用 sample_weight [英] Using sample_weight in GridSearchCV

问题描述

是否可以执行 GridSearchCV(以获得最佳 SVM 的 C)并使用 scikit-learn 指定 sample_weight?

这是我的代码和我遇到的错误:

gs = GridSearchCV(支持向量机.SVC(C=1),[{'内核':['线性']，'C': [.1, 1, 10],'概率':[真]，'sample_weight':sw_train，}])gs.fit(Xtrain, ytrain)

<块引用>

>>ValueError:估计器 SVC 的参数 sample_weight 无效

<小时>

我通过获取最新的 scikit-learn 版本并使用以下内容解决了这个问题:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

解决方案

只是想解决这个悬而未决的问题...

您需要获取最新版本的 SKL 并使用以下内容:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

但是，将fit_params传递给构造函数更符合文档:

gs = GridSearchCV(svm.SVC(C=1), [{'kernel': ['linear'], 'C': [.1, 1, 10], 'probability': [True], 'sample_weight': sw_train}], fit_params={'sample_weight': sw_train})gs.fit(Xtrain, ytrain)

Is it possible to perform a GridSearchCV (to get the best SVM's C) and yet specify the sample_weight with scikit-learn?

Here's my code and the error I'm confronted to:

gs = GridSearchCV(
    svm.SVC(C=1),
    [{
        'kernel': ['linear'],
        'C': [.1, 1, 10],
        'probability': [True],
        'sample_weight': sw_train,
    }]
)

gs.fit(Xtrain, ytrain)

>> ValueError: Invalid parameter sample_weight for estimator SVC

---

Edit: I solved the issue by getting the latest scikit-learn version and using the following:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

解决方案

Just trying to close out this long hanging question...

You needed to get the last version of SKL and use the following:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

However, it is more in line with the documentation to pass fit_params to the constructor:

gs = GridSearchCV(svm.SVC(C=1), [{'kernel': ['linear'], 'C': [.1, 1, 10], 'probability': [True], 'sample_weight': sw_train}], fit_params={'sample_weight': sw_train})

gs.fit(Xtrain, ytrain)

这篇关于在 GridSearchCV 中使用 sample_weight的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！