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Loss functions are related to model accuracy, a key component of AI/ML governance. We can design our own (very) basic loss function to further explain how it works. For each prediction that we make, our loss function will simply measure the absolute difference between our prediction and the actual value.
How to describe loss function in mathematical notation?
We can design our own (very) basic loss function to further explain how it works. For each prediction that we make, our loss function will simply measure the absolute difference between our prediction and the actual value. In mathematical notation, it might look something like abs (y_predicted – y).
Do you need a standard loss function for cross validation?
Particularly when computational methods like cross-validation are applied, there is no need to stick to “standard” loss functions such as the L2-loss (squared loss).
Are there loss functions that work for all kind of data?
There is not a single loss function that works for all kind of data. It depends on a number of factors including the presence of outliers, choice of machine learning algorithm, time efficiency of gradient descent, ease of finding the derivatives and confidence of predictions.
Where does the error come from in machine learning?
In supervised machine learning algorithms, we want to minimize the error for each training example during the learning process. This is done using some optimization strategies like gradient descent. And this error comes from the loss function. What’s the Difference between a Loss Function and a Cost Function?
How are loss functions used in optimization algorithms?
For most optimization algorithms, it is desirable to have a loss function that is globally continuous and differentiable. Two very commonly used loss functions are the squared loss, L ( a ) = a 2 {\\displaystyle L(a)=a^{2}} , and the absolute loss, L ( a ) = | a | {\\displaystyle L(a)=|a|} .