One versus One and One versus All multiclass classification using logistic regression in python

Question

This is my understanding of OvO versus OvA: One versus One is binary classification like Banana versus Orange. One versus All/Rest classification turns it into multiple different binary classification problems. My implementation in python for these 2 strategies yield very similar results :

OvA:

model = LogisticRegression(random_state=0, multi_class='ovr', solver='lbfgs')
model.fit(x,y)
model.predict(x)

OvO:

    model = LogisticRegression()
    model.fit(x,y)
    model.predict(x)

I wanted to confirm my understanding and implementation is correct since I get similar results. I need to implement OvO and OvA strategy for multiclass classification using logistic regression

The default value for Logistic Regression parameter **multi_class** is **'ovr'**. So when you're implementing OvO, by default the model uses 'ovr' . — thilakshiK, Oct 22 '19 at 14:43

score 0 · Answer 1 · answered Oct 28 '19 at 18:39

0

I ended up using the sklearn inbuilt class for oneVsRestClassifier and OneVsOneclassifier

answered Oct 28 '19 at 18:39

perplexedDev

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One versus One and One versus All multiclass classification using logistic regression in python

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