A loss function is a part of a cost function which is a type of an objective function. All that being said, thse terms are far from strict, and depending on context, research group, background, …

What are common cost functions used in evaluating the performance of neural networks? Details (feel free to skip the rest of this question, my intent here is simply to provide clarification on nota...

Jul 23, 2018 · 2 I am able to visualize how KNN works. Essentially take avg of the k nearest train neighbors for regression problem. However every ML algorithm optimizes a cost/loss function …

The cost function of a neural network is in general neither convex nor concave. This means that the matrix of all second partial derivatives (the Hessian) is neither positive semidefinite, nor …

Dec 15, 2017 · 10 In Decision Tree, splitting criterion methods are applied say information gain to split the current tree node to built a decision tree, but in many machine learning problems, …

I am trying to fit a SVM to my data. My dataset contains 3 classes and I am performing 10 fold cross validation (in LibSVM): ./svm-train -g 0.5 -c 10 -e 0.1 -v 10 training_data The help …

Jun 5, 2015 · I'm a bit confused with a lecture on linear regression given by Andrew Ng on Coursera about machine learning. There, he gave a cost function that minimises the sum-of …

Jun 5, 2020 · However, it is impractical to derive the cost function from actual costs every time you build a model, so we tend to gravitate to using one of the loss functions available in software.

May 22, 2018 · The objective function, which is the function that is to be minimized, can be constructed as the sum of cost function and regularization terms. In case both are independent …

May 11, 2017 · Here is another, in my opinion easy to follow, explanation of how the partial derivatives of the logistic regression cost function can be obtained.