APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Regression
|
|
|
Classification
|
|
|
None of these
|
|
|
Regression and Classification
|
Detailed explanation-1: -1. Mean Square Error / Quadratic Loss / L2 Loss. We define MSE loss function as the average of squared differences between the actual and the predicted value. It’s the most commonly used regression loss function.
Detailed explanation-2: -(1) Mean Squared Error (MSE) Advantage: The MSE is great for ensuring that our trained model has no outlier predictions with huge errors, since the MSE puts larger weight on theses errors due to the squaring part of the function.
Detailed explanation-3: -Mean Squared Error, commonly used for linear regression models, isn’t convex for logistic regression. This is because the logistic function isn’t always convex. The logarithm of the likelihood function is however always convex.