Intro to ML

1. Machine learning is ________ field.

• Inter-disciplinary

• Single

• Multi-disciplinary

• All of the above

A computer program is said to learn from __________ E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with E.

• Training

• Evolution

• Experience

• Enrichment

__________ has been used to train vehicles to steer correctly and autonomously on road.

• Robotics

• Neural networks

• Data mining

• Machine learning

The model learns and updates itself through reward/punishment in case of

• Supervised learning algorithm

• Unsupervised learning algorithm

• Semi-supervised learning algorithm

• Reinforcement learning algorithm

When the number of features increase

• Computation time increases

• Model becomes complex

• Learning accuracy decreases

• All of the above

Which of the following is not a supervised machine learning algorithm?

• K-means

• Naïve Bayes

• SVM for classification problems

• Decision tree

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.

In which category does linear regression belong to?

• Neither supervised nor unsupervised learning

• Both supervised and unsupervised learning

• Unsupervised learning

• Supervised learning

The target variable is represented along ____________

• Y axis

• X axis

• Either Y-axis or X-axis, it doesn’t matter

• Depends on the dataset

The learner is trying to predict the cost of papaya based on its size. The variable “cost” is

• independent variable

• target Variable

• ranked variable

• categorical variable