Machine Learning with Python Quiz Questions and Answers

If you want to predict the temperature of next day, what kind of problem it will be?

Answer :
  • Regression

Identify what type of problem this is. You have the past data of two cricket teams on the performance of the teams based on different parameters and the match results. You have to predict which team will win.

Answer :
  • Supervised Learning

Identify what type of problem this is. You feed a large collection of spam emails to the learning model to identify the different sub-groups of these spam mails. No labels are presents in the data set.

Answer :
  • Unsupervised Learning

Identify what type of problem this is. Consider a large data set of the medical profiles of cancer patients. This data contains no labels for the medical profiles of the cancer patients. The model has to learn whether there might be different groups of such patients for which separate treatments might be tailored.

Answer :
  • Unsupervised Learning

The coefficients of the least squares regression line are determined by the Ordinary Least Squares method — which basically means minimising the sum of the squares of the:

Answer :
  • y-coordinates of actual data - y-coordinates of predicted data

A Singapore-based startup Healin launched an app called JustShakeIt that enables a user to send an emergency alert to emergency contacts and/or caregivers simply by shaking the phone with one hand. The program uses a machine learning algorithm to distinguish between actual emergency shakes and everyday jostling, using data with labels to distinguish between everyday jostling and emergency shaking. What kind of problem is this?

Answer :
  • Classification

The independent variable X from a linear regression is measured in miles. If you convert it to kilometres (keeping the unit of the dependent Y variable same), how would the slope coefficient change? (1 mile = 1.6 km)

Answer :
  • It would get divided by 1.6

In the simple linear regression model between TV and sales, the accuracy, or the 'model fit', as measured by R-squared was about 0.81. But, when you brought in the radio and the newspaper variables along with TV, the R-squared increased to 0.91 and 0.83, respectively. Do you think the R-squared value will always increase (or at least remain the same) when you add more variables?

Answer :
  • Yes

What is the disadvantage of decision trees?

Answer :
  • Decision trees are prone to be overfit

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

Answer :
  • All of the above