Machine Learning Quiz 1 Flashcards
Which of the following is a classification task?
A. Detect pneumonia from chest X-ray image
B. Predict the price of a house based on floor area, number of rooms etc.
C. Predict the temperature for the next day
D. Predict the amount of rainfall
A. Detect pneumonia from chest X-ray image
B. Predict the price of a house based on floor area, number of rooms etc.
C. Predict the temperature for the next day
D. Predict the amount of rainfall
The correct answer is to detect pneumonia from a chest X-ray image, which is a classification task.
Which of the following is not a type of supervised learning?
A. Classification
B. Regression
C. Clustering
D. None of the above
A. Classification
B. Regression
C. Clustering
D. None of the above
The correct answer is clustering, which is not a type of supervised learning.
Which of the following tasks is NOT a suitable machine learning task?
A. Finding the shortest path between a pair of nodes in a graph
B. Predicting if a stock price will rise or fall
C. Predicting the price of petroleum
D. Grouping mails as spams or non-spams
A. Finding the shortest path between a pair of nodes in a graph
B. Predicting if a stock price will rise or fall
C. Predicting the price of petroleum
D. Grouping mails as spams or non-spams
The task that is NOT suitable for machine learning is finding the shortest path between a pair of nodes in a graph.
Suppose I have 10,000 emails in my mailbox out of which 300 are spams. The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system?
The precision of the spam detection system is 33.33%, and the recall is 16.66%.
Which of the following is/are supervised learning problems?
A. Predicting disease from blood samples.
B. Grouping students in the same class based on similar features.
C. Face recognition to unlock your phone.
A. Predicting disease from blood samples.
B. Grouping students in the same class based on similar features.
C. Face recognition to unlock your phone.
The supervised learning problems are Predicting disease from blood samples and Face recognition to unlock your phone.
Aliens challenge you to a complex game that no human has seen before. They give you time to learn the game and develop strategies before the final showdown. You choose to use machine learning because an intelligent machine is your only hope. Which machine learning paradigm should you choose for this?
The best machine learning paradigm for this scenario is reinforcement learning.
How many Boolean functions are possible with N features?
The number of Boolean functions possible with N features is 22N.
What is the use of Validation dataset in Machine Learning?
A. To train the machine learning model.
B. To evaluate the performance of the machine learning model
C. To tune the hyperparameters of the machine learning model
A. To train the machine learning model.
B. To evaluate the performance of the machine learning model
C. To tune the hyperparameters of the machine learning model
The Validation dataset is used to tune the hyperparameters of the machine learning model.
Regarding bias and variance, which of the following statements are true?
A. Models which overfit have a high bias.
B. Models which overfit have a low bias.
C. Models which underfit have a high variance.
D. Models which underfit have a low variance.
A. Models which overfit have a high bias.
B. Models which overfit have a low bias.
C. Models which underfit have a high variance.
D. Models which underfit have a low variance.
The true statements are Models which overfit have a low bias and Models which underfit have a low variance.
Which of the following is a categorical feature?
A. Height of a person
B. Price of petroleum
C. Mother tongue of a person
D. Amount of rainfall in a day
A. Height of a person
B. Price of petroleum
C. Mother tongue of a person
D. Amount of rainfall in a day
The categorical feature among the options is Mother tongue of a person.
In a binary classification problem, out of 30 data points 12 belong to class I and 18 belong to class II. What is the entropy of the data set?
Α. 0.97
B. 0
C. 1
D. 0.67
Α. 0.97
B. 0
C. 1
D. 0.67
The entropy of the data set is 0.97.
Which of the following properties are characteristics of decision trees?
A. Low bias
B. High variance
C. Lack of smoothness of prediction surfaces
D. None of the above
A. Low bias
B. High variance
C. Lack of smoothness of prediction surfaces
D. None of the above
The characteristics of decision trees are low bias, high variance, and lack of smoothness of prediction surfaces.
Statement: Decision Tree is an unsupervised learning algorithm.
Reason: The splitting criterion uses only the features of the data to calculate their respective measures.
Reason: The splitting criterion uses only the features of the data to calculate their respective measures.
The statement is false and the reason is false.
What is a common indicator of overfitting in a decision tree?
A. The training accuracy is high while the test accuracy is low.
B. The tree is shallow.
C. The tree has only a few leaf nodes.
D. The tree’s depth matches the number of attributes in the dataset.
A. The training accuracy is high while the test accuracy is low.
B. The tree is shallow.
C. The tree has only a few leaf nodes.
D. The tree’s depth matches the number of attributes in the dataset.
A common indicator of overfitting in a decision tree is the training accuracy is high while the test accuracy is low.
What is true for Batch Gradient Descent?
A. In every iteration, model parameters are updated based on one training sample
B. In every iteration, model parameters are updated based on all training samples
A. In every iteration, model parameters are updated based on one training sample
B. In every iteration, model parameters are updated based on all training samples
For Batch Gradient Descent, model parameters are updated based on all training samples in every iteration.
Which of the following criteria is typically used for optimizing in linear regression?
A. Maximizing the number of points touched by the line
B. Minimizing the number of points touched by the line
C. Minimizing the sum of squared distance of the line from the points
A. Maximizing the number of points touched by the line
B. Minimizing the number of points touched by the line
C. Minimizing the sum of squared distance of the line from the points
In linear regression, the typical optimization criterion is minimizing the sum of squared distance of the line from the points.
The parameters obtained in linear regression
A. can take any value in the real space
B. are strictly integers
C. always lie in the range [0,1]
D. can take only non-zero values
A. can take any value in the real space
B. are strictly integers
C. always lie in the range [0,1]
D. can take only non-zero values
The parameters obtained in linear regression can take any value in the real space.
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