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machine-learningloss-functioncatboost
swapnil agashe
swapnil agashe

Reputation: 69

Is the loss function='Multiclass' in catboost same as log loss if I am doing a multiclassification problem?

I am making a multiclass prediction model using catboost, The final solution should have minimum Logloss error but Logloss is not present in catboost, they have something called 'Multiclass' as the loss function. Are they both same? if not then how can I measure the accuracy of the catboost model in terms of Logloss?

Upvotes: 0

Views: 6804

Answers (2)

caldweln
caldweln

Reputation: 134

Are they both same? Effectively, Yes...

The catboost documentation describe the calculation of 'MultiClass' loss as what is generally considered as Multinomial/Multiclass Cross Entropy Loss. That is effectively, a Log Softmax applied to the classifier output 'a' to produce values that can be interpreted as probabilities, and subsequently then apply Negative Log Likelihood Loss (NLLLoss), wiki1 & wiki2.

Their documentation describe the calculation of 'LogLoss' also, which again is NLLLoss, however applied to 'p'. Which they describe here to be result of applying the sigmoid fn to the classifier output. Since the NLLLoss is reworked for the binary problem, only a single class probability is calculated, using 'p' and '1-p' for each class. And in this special (binary) case, use of sigmoid and softmax are equivalent.

How can I measure the the catboost model in terms of Logloss?

They describe a method to produce desired metrics on given data.

Be careful not to confuse loss/objective function 'loss_function' with evaluation metric 'eval_metric', however in this instance, the same function can be used for both, as listed in their supported metrics.

Hope this helps!

Upvotes: 4

Jodh Singh
Jodh Singh

Reputation: 383

Log loss is not a loss function but a metric to measure the performance of a classification model where the prediction is a probability value between 0 and 1. Learn more here.

Upvotes: 0

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