Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?

You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power.

Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
A . Deep learning neural network
B . Random forest
C . Ridge regression
D . Support-vector machine

View Answer

Answer: C

Explanation:

Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.