CODEWITHSUNDEEP
pythonmachine-learningscikit-learnclassificationcross-validation
EmJ
EmJ

Reputation: 4618

How to get multi-class roc_auc in cross validate in sklearn?

I have a classification problem where I want to get the roc_auc value using cross_validate in sklearn. My code is as follows. 

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier(random_state = 0, class_weight="balanced")

from sklearn.model_selection import cross_validate
cross_validate(clf, X, y, cv=10, scoring = ('accuracy', 'roc_auc'))

However, I get the following error.

ValueError: multiclass format is not supported

Please note that I selected roc_auc specifically is that it supports both binary and multiclass classification as mentioned in: https://scikit-learn.org/stable/modules/model_evaluation.html

I have binary classification dataset too. Please let me know how to resolve this error.

I am happy to provide more details if needed.

Upvotes: 2

Views: 5110

Answers (1)

seralouk
seralouk

Reputation: 33197

By default multi_class='raise' so you need explicitly to change this.

From the docs:

multi_class {‘raise’, ‘ovr’, ‘ovo’}, default=’raise’

Multiclass only. Determines the type of configuration to use. The default value raises an error, so either 'ovr' or 'ovo' must be passed explicitly.

'ovr':

Computes the AUC of each class against the rest [3] [4]. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when average == 'macro', because class imbalance affects the composition of each of the ‘rest’ groupings.

'ovo':

Computes the average AUC of all possible pairwise combinations of classes [5]. Insensitive to class imbalance when average == 'macro'.

Solution:

Use make_scorer (docs):

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier(random_state = 0, class_weight="balanced")

from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score

myscore = make_scorer(roc_auc_score, multi_class='ovo',needs_proba=True)

from sklearn.model_selection import cross_validate
cross_validate(clf, X, y, cv=10, scoring = myscore)

Upvotes: 6

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