Contents
What is the optimal classifier?
The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. Bayes Optimal Classifier is a probabilistic model that finds the most probable prediction using the training data and space of hypotheses to make a prediction for a new data instance.
Does naive Bayes have a loss function?
The loss function of naive Bayes is always the negative joint log-likelihood, -log p(X, Y). This choice of loss function, under the naive Bayes assumption of feature independence, makes naive Bayes fast: maximum-likelihood training can be done by performing one matrix multiplication and a few sums.
Which is the optimal classifier for k = 1?
We can see that if k = 1 which is just the 0-1 loss, the optimal classifier has: which is exactly f ∗ (x) = argmaxP(G = k | x), same as before. Assume we use 0-1 loss. Now we know that the optimal classifier maximizes the posterior.
Which is the best description of the Bayes optimal classifier?
The Bayes optimal classifier is a probabilistic model that makes the most probable prediction for a new example, given the training dataset. This model is also referred to as the Bayes optimal learner, the Bayes classifier, Bayes optimal decision boundary, or the Bayes optimal discriminant function.
Which is the optimal classifier in decision theory?
It introduces Decision Theory, Bayes’ Theorem, and how we can derive out the Bayes Classifier, which is the optimal classifier in theory that leads to the lowest misclassification rate. This is probably the most fundamental theory in Statistics. Let’s review it briefly: Where A, B represent event or variable probabilities.
Which is better, an optimization function or a loss function?
Both, Adagrad and Adam produced better results that SGD, but they were computationally extensive. Adam was slightly faster than Adagrad. Thus, while using a particular optimization function, one has to make a trade off between more computation power and more optimum results.