Question

chooose all tye corrct answers to the question:-

Q1) What are the best practices for building out good unit tests?

- You do not need an assert statement for each edge case.

- Write a test function to check for implementation given all parameters.

- Have multiple assert statements to test for all edge cases.

- Think about all edge cases before you write the function.

- Run a test function to see if your function works in all edge cases.

Q2) Which one of the following statements is INCORRECT?

- In regression models, input and output variables have numerical values.

- When fitting the line, we are trying to maximize the distance of each point to the line.

- We can make predictions if input values are given and the equation of the line is known.

- Although it is called a linear regression model, any curve can be used as long as it can fit the data well.

Q3) "The following descriptions best describe what: 1. Value that has to be assigned manually. 2. The K value in K-nearest-neighbore is an example of this. 3. Value is set before the training."

- Model Parameter

- Model hyperparameter

- Significance

- Centroid

Q4) Which of the following describes code that is CLEAN?

- Simple

- Readable

- Vague

- Concise

- Repetitive

Q5)What are some useful metrics to evaluate a GAN’s performance?

- Precision

- Discriminator and generator loss

- Recall

- Softmax

Answer 1

Answer)

1) One should think about all edge cases much before one writes the function

For writting a test case one needs to identifying the test cases and their functions

2) One can make predictions incase if input values are provided and the equation of the line is known

One needs to have a bench value for any predictions when input value is present

3) Model hyperparameter

When considering the machine learning, where the hyperparameter is considered to be a parameter whose value would be used for controlling the learning process where values with the other parameter would be derived via the training.

4) Concise

Which would provide with crisp and clear codes which is clean

5) Precision

It is the inception distance and score which is meant for evaluation of generated image.

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