logisticRegress（）vs logisticRegressioncv（）及其CS超参数
我已经使用logisticRegress（）构建了一个模型，并且在网格搜索之后，数据建议我的正规化强度，c = .0000001是“最佳”值做出我的预测。
此参数适用于logisticRegression（），但是当我想跨validate时，我决定使用logisticRegressioncv（）等效 c parameter这表示为cs，但是当我尝试传递相同的变量cs = .0000001时，我会遇到一个错误：
797 warm_start_sag = {"coef": np.expand_dims(w0, axis=1)}
799 coefs = list()
--> 800 n_iter = np.zeros(len(Cs), dtype=np.int32)
801 for i, C in enumerate(Cs):
802 if solver == "lbfgs":
TypeError: object of type 'float' has no len()
当引用 documents 似乎对于logistic> logistic> logistic> logistic> logistic> logistic> logistic>
如果CS是INT，则在A中选择CS值的网格 1E-4和1E4之间的对数尺度。
然后，我仍将如何输入CS = .0000001的值？我对如何进行感到困惑。
I've built a model using LogisticRegression() and after a grid search the data suggests for my inverse of regularization strength, C = .0000001 is the "best" value to make my predictions.
This parameter works fine for LogisticRegression(), but seeing as I want to cross-validate I decide to use LogisticRegressionCV() the equivalent c parameter here is denoted as Cs, yet when I try to pass the same variable Cs = .0000001, I get an error:
797 warm_start_sag = {"coef": np.expand_dims(w0, axis=1)}
799 coefs = list()
--> 800 n_iter = np.zeros(len(Cs), dtype=np.int32)
801 for i, C in enumerate(Cs):
802 if solver == "lbfgs":
TypeError: object of type 'float' has no len()
When referring to the documents it seems that for LogisticRegressionCV():
If Cs is as an int, then a grid of Cs values are chosen in a
logarithmic scale between 1e-4 and 1e4.
How would I then still input a value of Cs = .0000001? I'm confused about how to proceed.
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logisticRegressioncv不仅仅是交叉验证量表的逻辑回归；它是一个高参数调整（通过交叉验证）逻辑回归。也就是说，它尝试了几种不同的正规化强度，并使用交叉验证分数选择最佳的强度（然后使用最佳c在整个训练集上改进单个模型）。cs可以是尝试c的值列表，也可以是让Sklearn为您创建列表的整数（如您的引用文档中）。如果您只想用固定的
c为模型评分，请使用cross_val_score或cross_validate。（您可能 can 使用
logisticRegressioncv，设置cs = [0.0000001]，但这不是正确的语义用法。）LogisticRegressionCVis not meant to be just cross-validation-scored logistic regression; it is a hyperparameter-tuned (by cross-validation) logistic regression. That is, it tries several different regularization strengths, and selects the best one using cross-validation scores (then refits a single model on the entire training set, using that bestC).Cscan be a list of values to try forC, or an integer to let sklearn create a list for you (as in your quoted doc).If you just want to score your model with fixed
C, usecross_val_scoreorcross_validate.(You probably can use
LogisticRegressionCV, settingCs=[0.0000001], but it's not the right semantic usage.)