估算器SVR的无效参数丢失 [英] Invalid Parameter loss for estimator SVR

问题描述

这是我的代码

我使用网格搜索cv进行超参数调整.但显示错误.

i used grid search cv for hyper parameter tuning. but it shows error.

param_grid = {"kernel" : ['linear', 'poly', 'rbf', 'sigmoid'],
            'loss' : ['epsilon_insensitive', 'squared_epsilon_insensitive'],
             "max_iter" : [1,10,20],
             'C' : [np.arange(0,20,1)]} 

model = GridSearchCV(estimator = svr, param_grid = param_grid, cv = 5, verbose = 3, n_jobs = -1)

m1 = model.fit(x_train,y_train)

ValueError: Invalid parameter loss for estimator SVR(C=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
   17, 18, 19]),
kernel='linear'). Check the list of available parameters with `estimator.get_params().keys()`.

推荐答案

我发现了一些错误:

• 您似乎正在指定仅为LinearSVR，而不是 SVR.另一方面，如果您要使用 LinearSVR ，则不能指定内核，因为它必须是线性的.

• You seem to be specifying a loss parameter and possible values, that are only defined for a LinearSVR, not a SVR. On another hand, if you do want to use a LinearSVR, you can't specify a kernel, since it has to be linear.

我还注意到网格定义中的'C':[np.arange(0,20,1)] 会产生错误，因为它会导致嵌套列表.只需使用 np.arange(0,20,1)

I also noticed that 'C' : [np.arange(0,20,1)] in the definition of the grid will yield an error, since it results in a nested list. Just use np.arange(0,20,1)

假设您有一个 SVR ，以下内容将为您工作:

Assuming then you have a SVR, the following should work for you:

from sklearn.svm import SVR
svr = SVR()

param_grid = {"kernel" : ['linear', 'poly', 'rbf', 'sigmoid'],
             "max_iter" : [1,10,20],
             'C' : np.arange(0,20,1)} 

model = GridSearchCV(estimator = svr, param_grid = param_grid, 
                     cv = 5, verbose = 3, n_jobs = -1)
m1 = model.fit(X_train, y_train)

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