Does GridSearchCV not support multi-class?

Question

I tried to use GridSearchCV for multi-class case based on the answer from here:

Accelerating the prediction

But I got value error, multiclass format is not supported.

How can I use this method for multi-class case?

Following code is from the answer in above link.

import numpy as np
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score, recall_score, f1_score, roc_auc_score, make_scorer

X, y = make_classification(n_samples=3000, n_features=5, weights=[0.1, 0.9, 0.3])

pipe = make_pipeline(StandardScaler(), SVC(kernel='rbf', class_weight='auto'))

param_space = dict(svc__C=np.logspace(-5,0,5), svc__gamma=np.logspace(-2, 2, 10))

accuracy_score, recall_score, roc_auc_score
my_scorer = make_scorer(roc_auc_score, greater_is_better=True)

gscv = GridSearchCV(pipe, param_space, scoring=my_scorer)
gscv.fit(X, y)

print gscv.best_params_

Answer 1

From the documentation on roc_auc_score:

Note: this implementation is restricted to the binary classification task or multilabel classification task in label indicator format.

By "label indicator format", they mean each label value is represented as a binary column (rather than as a unique target value in a single column). You don't want to do that for your predictor, as it could result in non-mutually-exclusive predictions (i.e., predicting both label 2 and 4 for case p1, or predicting no labels for case p2).

Pick or custom-implement a scoring function that is well-defined for the multiclass problem, such as F1 score. Personally I find informedness more convincing than F1 score, and easier to generalize to the multiclass problem than roc_auc_score.

Answer 2

It supports multi-class You can set the para of scoring = f1.macro, example:

gsearch1 = GridSearchCV(estimator = est1, param_grid=params_test1, scoring='f1_macro', cv=5, n_jobs=-1)

Or scoring = 'roc_auc_ovr'