RMSE AND MSE

Question 1

use of performance metrics

Answer 1

Performance metrics like classification accuracy and root mean squared error can give you a clear objective idea of how good a set of predictions is

Question 2

objective function

Answer 2

All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”.

Question 3

loss functions

Answer 3

The group of functions that are minimized are called “loss functions”
- A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome.

Question 4

gradient descent

Answer 4

A most commonly used method of finding the minimum point of function is “gradient descent”.

Question 5

Loss functions can be broadly categorized into 2 types:

Answer 5

Classification and Regression Loss.

Question 6

Regression functions

Answer 6

predict a quantity

Question 7

classification functions

Answer 7

predict a label

Question 8

RMSE (Root Mean Squared Error)

Answer 8

one of the methods to determine the accuracy of our model in predicting the target values.In machine Learning when we want to look at the accuracy of our model we take the root mean square of the error that has occurred between the test values and the predicted values mathematically

Question 9

MSE (Mean Squared Error)

Answer 9

Mean Square Error (MSE) is the most commonly used regression loss function.
MSE is the sum of squared distances between our target variable and predicted values

Question 10

Why use mean squared error

Answer 10

MSE is sensitive towards outliers and given several examples with the same input feature values, the optimal prediction will be their mean target value. This should be compared with Mean Absolute Error, where the optimal prediction is the median. MSE is thus good to use if you believe that your target data, conditioned on the input, is normally distributed around a mean value, and when it’s important to penalize outliers extra much.

Question 11

When to use mean squared error

Answer 11

Use MSE when doing regression, believing that your target, conditioned on the input, is normally distributed, and want large errors to be significantly (quadratically) more penalized than small ones.

Question 12

Classification loss

Answer 12

log loss, focal loss, KL Divergence/Relative Entropy, Exponential loss, Hinge loss

Question 13

Regression loss

Answer 13

MSE loss/quadratic loss, Mean Absolute error, Log cosh loss, Huber loss/Smooth Mean Absolute error, quantile loss