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I want to perform GridSearchCV in a SVC model, but that uses the one-vs-all strategy. For the latter part, I can just do this:

model_to_set = OneVsRestClassifier(SVC(kernel="poly"))

My problem is with the parameters. Let's say I want to try the following values:

parameters = {"C":[1,2,4,8], "kernel":["poly","rbf"],"degree":[1,2,3,4]}

In order to perform GridSearchCV, I should do something like:

 cv_generator = StratifiedKFold(y, k=10)

 model_tunning = GridSearchCV(model_to_set, param_grid=parameters, score_func=f1_score, n_jobs=1, cv=cv_generator)

However, then I execute it I get:

Traceback (most recent call last):

  File "/.../main.py", line 66, in <module>

    argclass_sys.set_model_parameters(model_name="SVC", verbose=3, file_path=PATH_ROOT_MODELS)

ValueError: Invalid parameter kernel for estimator OneVsRestClassifier

Basically, since the SVC is inside a OneVsRestClassifier and that's the estimator I send to the GridSearchCV, the SVC's parameters can't be accessed.

In order to accomplish what I want, I see two solutions:

When creating the SVC, somehow tell it not to use the one-vs-one strategy but the one-vs-all.

Somehow indicate the GridSearchCV that the parameters correspond to the estimator inside the OneVsRestClassifier.

I'm yet to find a way to do any of the mentioned alternatives. Do you know if there's a way to do any of them? Or maybe you could suggest another way to get to the same result?


1 Answer

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by (33.2k points)

Nested estimators using with grid search can scope the parameters with __ as a separator. In your case, the SVC model is stored as an attribute named estimator inside the OneVsRestClassifier model.

For example:

from sklearn.datasets import load_iris

from sklearn.multiclass import OneVsRestClassifier

from sklearn.svm import SVC

from sklearn.grid_search import GridSearchCV

from sklearn.metrics import f1_score

iris = load_iris()

model_to_set = OneVsRestClassifier(SVC(kernel="poly"))

parameters = {

    "estimator__C": [1,2,4,8],

    "estimator__kernel": ["poly","rbf"],

    "estimator__degree":[1, 2, 3, 4],


model_tunning = GridSearchCV(model_to_set, param_grid=parameters,


model_tunning.fit(iris.data, iris.target)

print model_tunning.best_score_

print model_tunning.best_params_



{'estimator__kernel': 'poly', 'estimator__C': 1, 'estimator__degree': 2}

Hope this answer helps.

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