IT B MLA Lab Quiz-1

Which of the following is an example of an unsupervised learning algorithm?

• Linear Regression

• K-Means Clustering

• Decision Trees

• Support Vector Machines

What is the key difference between supervised and unsupervised learning?

• The presence of labeled data in supervised learning

• The use of deep neural networks in unsupervised learning

• The requirement for large datasets in supervised learning

• The absence of algorithms in unsupervised learning

What is overfitting in the context of machine learning models?

• Fitting a model with insufficient data

• Fitting a model too closely to the training data

• Fitting a model with too few features

• Fitting a model to the validation set

S = <sunny, warm, high, same>. Training data = <sunny, warm, normal, same> => Yes (positive example). How will S be represented after encountering this training data?

• <sunny, warm, high, same>

• <phi, phi, phi, phi>

• <sunny, warm, ?, same>

• <sunny, warm, normal, same>

S = <phi, phi, phi, phi>Training data = <rainy, cold, normal, change> => No (negative example). How will S be represented after encountering this training data?

• <phi, phi, phi, phi>
  
  

• <sunny, warm, high, same>

• <rainy, cold, normal, change>
  
  

• <?, ?, ?, ?>

What is one of the assumptions of the Find-S algorithm?

• No assumptions are made
  
  

• The most specific hypothesis is also the most general hypothesis
  
  

• All training data are correct (there is no noise)

• Overfitting does not occur

In simple term, machine learning is

• training based on historical data

• prediction to answer a query

• both A and B

• automization of complex task

What is Machine learning?

• The autonomous acquisition of knowledge through the use of computer programs
  
  

• The autonomous acquisition of knowledge through the use of manual programs

• The selective acquisition of knowledge through the use of computer programs

• The selective acquisition of knowledge through the use of manual programs.

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.

What is the goal of concept learning?

• To minimize cross-validation set error

• To maximize test set accuracy
  
  

• To find a hypothesis that is most suitable for training instances

• To identify all possible predictors