NKV Machine Learning

Q learning is a type of learning that

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• None

If machine learning model output involves target variable then that model is called as

• Descriptive model

• Predictive model

• 
  Reinforcement learning

• All

Data used to build a data mining model.

• training data

• validation data

• 
  test data

• hidden data

Of the Following Examples, Which would you address using an supervised learning Algorithm?

• 
  given email labeled as spam or not spam, learn a spam filter

• 
  given a set of news articles found on the web, group them into set of articles about the same story.

• 
  given a database of customer data, automatically discover market segments and group customers into different market segments.

• 
  find the patterns in market basket analysis

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

• 
  semisupervised learning

• 
  unsupervised learning

• 
  supervised learning

• Reinforcement learning

The output of training process in machine learning is

• machine learning model

• machine learning algorithm

• NULL

• accuracy

K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?

• Instance-based learning

• Parametric learning

• Non-Parametric learning

• Model-based learning

What is the key difference between supervised and unsupervised learning?

• Supervised learning requires labeled data, while unsupervised learning does not.

• Supervised learning predicts labels, while unsupervised learning discovers patterns.

• Supervised learning is used for classification, while unsupervised learning is used for regression.

• Supervised learning is always more accurate than unsupervised learning.

Which of the following statements is false about Ensemble learning?

• It is a supervised learning algorithm

• It is a unsupervised learning algorithm

• More random algorithms can be used to produce a stronger ensemble

• Ensembles can be shown to have more flexibility in the functions they can represent

Impact of high variance on the training set ?

• underfitting

• overfitting

• 
  both underfitting & overfitting

• None