The mean squared error (MSE) loss function cannot be used for classification problems.
The mean squared error (MSE) loss function is primarily used for regression problems, where the goal is to minimize the difference between the predicted and actual continuous values. For classification problems, where the target output is categorical (e.g., binary or multi-class labels), loss functions like cross-entropy are more suitable, as they are designed to handle the probabilistic interpretation of outputs in classification tasks.
Using MSE for classification could lead to inefficient training because it doesn't capture the probabilistic relationships required for classification tasks.
Viola
2 months agoGlory
14 days agoOzell
20 days agoTeri
23 days agoShannon
24 days agoAhmad
2 months agoLashawna
2 months agoDonte
2 months agoTiera
25 days agoDorethea
29 days agoCarylon
1 months agoChristoper
1 months agoAlba
2 months agoJean
3 months agoAlba
3 months ago