03.02.24 ML Algorithms

PCA is_________________

• 1. backward feature selection

  2.  

  4.  

• 1. forward feature selection

• 1. feature extraction

• 1. None of these

Supervised learning and unsupervised clustering both require which is correct according to the statement.

• 1.  input attribute

• 1.  hidden attribute

• 1. output attribute

• 1. categorical attribute

Following are the types of supervised learning________

• 1. regression

• 1. classification

• 1. subgroup discovery

• 1. All of above

A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is_______________

• 1. ordinal

• 1. nominal

• 1. categorical

• 1. boolean

Following is powerful distance metrics used by Geometric model_________________

• 1.  manhattan distance

• 1. euclidean distance

• 1. All of above

• 1. None of above

The output of training process in machine learning is________________

• 1. machine learning algorithm

• 1. machine learning model

• 1. null

• 1. accuracy

You are given reviews of few Netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of____________

• 1. unsupervised learning

• 1. semi supervised learning

• 1. supervised learning

• 1. reinforcement learning

The problem of finding hidden structure in unlabeled data is called______________

• 1. unsupervised learning

• 1. reinforcement learning

• 1. supervised learning

• 1. None

What does dimensionality reduction reduce?

• 1. collinearity

• 1. stochastic

• 1. entropy

• 1. performance

Some telecommunication company wants to segment their customers into distinct groups ,this is an example of________________

• 1. supervised learning

• 1. unsupervised learning

• 1. data extraction

• reinforcement learning