dsv ut-1

Which of the following is not a type of supervised learning?

• Classification

• 
  Regression

• 
  Clustering

• 
  None

Adding a new feature to the model always results in equal or better performance on the training set?

• True

• False

Which of the following are not classification tasks ?

• 
  Predict whether there will be abnormally heavy rainfall next year

• Predict the price of a house based on floor area, number of rooms etc.

• 
  Find the gender of a person by analyzing his writing style

• Detect Pneumonia from Chest X-ray imagesSelect

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 statements is true in the following case?

• Feature F1 is an example of a nominal variable.

• Feature F1 is an example of ordinal variables.

• It doesn’t belong to any of the above categories.

• quatative Variable

Which of the following is correct for reinforcement learning?

• The algorithm plans a sequence of actions from the current state.

• The training instances contain examples of states and best actions of the states.

• The algorithm plans one action at each time step.

• The algorithm groups unseen data based on similarity.

The goal of clustering a set of data is to?

• choose the best data from the set

• 
  determine the nearest neighbors of each of the data

• predict the class of data

• 
  divide them into groups of data that are near each other

I have 4 variables in the dataset such as – A, B, C & D. I have performed the following actions:

Step 1: Using the above variables, I have created two more variables, namely E = A + 3 B and F = B + 5 C + D.

Step 2: Then using only the variables E and F I have built a Random Forest model.

Could the steps performed above represent a dimensionality reduction method?

• TRUE

• 
  FALSE

How do you handle missing or corrupted data in a dataset?

• Drop missing rows or columns

• imputation can be done

• Assign a unique category to missing values

• All the above

Which of the below is not supervised learning?

• 
  Spam email identification

• House price prediction

• Speech recognition

• Amazon product suggestions

What is the relationship between model and algorithm?

• Algorithm + model = data

• Algorithm = Model + data

• 
  Model = Algorithm(data)

• Data = Algorithm(model)