COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Feature F1 is an example of nominal variable.
|
|
|
Feature F1 is an example of ordinal variable.
|
|
|
It doesn’t belong to any of the above category.
|
|
|
Both (a) and (b)
|
Detailed explanation-1: -A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case? A. Feature F1 is an example of nominal variable.
Detailed explanation-2: -Feature F1 is an example of ordinal variable.
Detailed explanation-3: -Each step of gradient descent will always decrease the value of the function.
Detailed explanation-4: -Overfitting and Underfitting. Overfitting: Overfitting is one of the most common issues faced by Machine Learning engineers and data scientists.
Detailed explanation-5: -The only condition in Stochastic Gradient Descent is that expected value of the observation picked at random is a subgradient of the function at point w[4]. Compared to Gradient Descent, Stochastic Gradient Descent is much faster, and more suitable to large-scale datasets.