Machine learning

A feature F1 can take a certain value: A, B, C, D, E, or F, which represents the grades of students from a college.

Which of the following statement is true in the following case?

• Feature F1 is an example of a nominal variable.

• Feature F1 is an example of an ordinal variable.

• It doesn’t belong to any of the above categories.

• Both of these

Suppose you want to develop a machine learning algorithm that predicts the number of views on the articles in a blog.

Your data analysis is based on features like author name, number of articles written by the same author, etc. Which of the following evaluation metrics would you choose in that case?

• 1. 
     Mean Square Error

• 1. Accuracy

• 1. F1 Score

• none

In ensemble learning, you aggregate the predictions for weak learners so that an ensemble of these models will give a better prediction than the prediction of individual machine learning models.

Which of the following statements is / are true for weak learners used in the ensemble model?

1. 1.They don’t usually overfit.

2. 2.They have high bias, so they cannot solve complex learning problems

3. 3.They usually overfit.

• 
   1 and 2

• 1 and 3

   

• 2 and 3

•  Only 1

Which of the following is an example of a classification problem?

• 
  Predicting the price of a house based on its features

• Predicting the weight of a person based on their height

• Predicting whether a customer will churn or not

• Predicting the age of a person based on their income

Which of the following is an example of a clustering algorithm?

• 
  Decision tree

• Random forest

• K-means

• Gradient descent

Which of the following is an example of a supervised learning problem?

• 
  Image classification

• Market segmentation

• Fraud detection

• Social network analysis

You are working on a project for a healthcare organization that wants to predict the likelihood of patients developing a specific medical condition based on their medical history and lifestyle factors. The organization values model interpretability and wants to be able to explain predictions to doctors and patients.

Which machine learning algorithm would be the most suitable choice for this healthcare project, given the emphasis on interpretability?

• Random Forest

• Support Vector Machine (SVM)

• Neural Network

• Logistic Regression

You are working on a spam email classification project for an email service provider. The goal is to automatically classify incoming emails as either "spam" or "not spam" (ham) based on their content and features. The organization values high accuracy in spam detection to ensure that genuine emails are not misclassified as spam.

Which machine learning algorithm would you recommend for the spam email classification project described above, considering the need for high accuracy in spam detection?

• Support Vector Machine (SVM)

• Random Forest

• Naive Bayes

• None of the above

Which type of probability is used in Naive Bayes to make predictions?

• 
  Prior Probability

• Conditional Probability

• Joint Probability

• Marginal Probability

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

• unsupervised learning

• semi supervised learning

• supervised learning

• reinforcement learning