Contents
What is a proper loss function?
Such loss functions where the posterior probability can be recovered using the invertible link are called proper loss functions. This holds even for the nonconvex loss functions, which means that gradient descent based algorithms such as gradient boosting can be used to construct the minimizer.
What is the difference between objective function and loss function?
The function we want to minimize or maximize is called the objective function, or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.
Why do we need a loss function in ML?
A loss function plays a key role when training (optimizing) ML models. It essentially calculates how good the model is at making predictions using a given set of values (i.e., weights and biases).
How to choose loss functions when training deep?
This requires the choice of an error function, conventionally called a loss function, that can be used to estimate the loss of the model so that the weights can Navigation Machine Learning MasteryMaking developers awesome at machine learning Click to Take the FREE Deep Learning Performane Crash-Course Home Main Menu Get Started Blog Topics
How to choose the correct regression loss function?
There are two common methods for dealing with the negative values, which are: Absolute value: taking the absolute value of the per-sample loss. 2. Square (power of 2): taking the square of the per-sample loss. Correspondingly, these are two of the most common regression loss functions:
How to choose the correct loss function for your neural network?
In the simple case where you have just one parameter, the loss function looks like the parabola figure below (convex loss function against a parameter value). In the case of neural networks, you can have millions or even billions of parameters, and the loss is a function of those, so it occupies a vast multi-dimensional space.