Question 1.
The result of a confusion matrix is as below:
Confusion Matrix Actual value
faulty(0) Good(1)
Prediction faulty(0) 50 3
Good(1) 5 42
What is the accuracy value?
Group of answer choices
92%
8%
50%
42%
Question 2.
A researcher has conducted a research to evaluate the relationship between age and the height of the students in a class. His results revealed the correlation of -1.0 between age and height. What is the conclusion of this research?
Group of answer choices
There is a strong negative relationship between age and height
There is a strong positive relationship between age and height
There is no relationship between age and height
There is a weak relationship between age and height
Question 3.
Which of the following statements is correct?
Group of answer choices
Multivariable and Multivariate regression are the same thing, i.e. each involve multiple features (independent variables) and one response (dependent variable).
There is no way to make a 2-way split on continuous data.
If we complete an analysis and find that there is statistically significant evidence of a relationship between a predictor variable and its corresponding response variable we can safely assert that we know the predictor caused the response.
Decision tree induction algorithms use what is called a greedy strategy but then may only find a local minimum.
Question 4:
Remember the dataset of alligators in Lecture 3 which was about the length and weight of several aligators in Florida. . The variable X is the length of aligator and the Y variable is the weight of them. A researcher decided to use decision tree and designed two steps: X4 . What is the name of this method of splitting?
Group of answer choices
Entropy classification
Gini index
Binary splitting
Multi-way splitting
Question 5:
Which one is NOT one of the disadvantages of decision tree?
Group of answer choices
In a complex model, it may result to an overfitting
Small variations in features may affect the trees
It is not useful when we have targets with more than 2 levels
If some of the target variables overcome the others, it may result in biased trees
Question 6:
What is the minimum value of Entropy in a decision tree and what does it mean?
Group of answer choices
0.5- Maximum impurity
0- Minimum impurity
0.5- Minimum impurity
0- Maximum impurity
Question 7:
Which one is NOT one of the gradient descent properties?
Group of answer choices
Works well even with larger number of instances
Needs many iterations
Need to choose an alpha (learning rate)
Need to compute transpose(X)*X
Question 8
In Gradient descent technique, we chose an alpha value (learning rate) in computation of parameters (theta zero and theta 1). What will happen if we assign a very small value to alpha?
Group of answer choices
The speed of the computations will be very high
The model computations may take a long time to converge
The model may never converge
There will be no need to iterate
Question 9:
Consider the following table which shows the satisfaction of customers from different stores:
Excellent Good Poor Total
Kmart 272 477 251 1000
Sears 315 457 228 1000
JCP 323 470 207 1000
Wards 391 404 205 1000
Total 1301 1808 891 4000
what is the probability that a customer find a store to be Good store if he/she is in JCP?
Group of answer choices
0.26
0.47
0.12
0.25
Question 10:
Which statement is NOT correct about Regression?
Group of answer choices
We don’t need to choose learning rate in Normal equation technique of regression
The goal in Gradient descent algorithm is to minimize the cost function
The cost function is the difference between the hypothesis and predicted output
The mathematics utilizing a cost function can be applied to either linear or logistic regression.
Question 11:
An artificial intelligence model is designed to predict a color of a shape with a unique color. During the training some pictures with three color (red,green and blue) are shown to machine and on each iteration, the correct color is given to machine. Now, this machine can predict red, green and blue colors. After training, a circle with color purple is shown to machine. What is the type of training and what will be the prediction of the model?
Group of answer choices
Supervised- red, blue or green
Unsupervised- red, blue or green
Unsupervised- purple
Supervised- purple
Question 12:
What type of learning is reflected in following figure and why?
Group of answer choices
Supervised because the class is known
Unsupervised because the target is unknown
Supervised because the learning algorithm is specified
Unsupervised because the training and testing sets are different
Question 13:
Which of the following examples is an example of unsupervised learning?
Group of answer choices
A face recognition in phone whereas your phone takes some pictures of you and next time it will only allow YOU to open the phone based on your face
A machine can analyze some x-rays and can predict if someone has cancer or not based on his/her x-ray.
Based on past information about spams, filtering out a new incoming email into Inbox (normal) or Junk folder (Spam)
NASA discovers new heavenly bodies and finds them different from previously known astronomical objects – stars, planets, asteroids, blackholes etc. (i.e. it has no knowledge about these new bodies) and categorize them the way it would like to (distance from Milky way, intensity, gravitational force, red/blue shift or whatever)
Qustion 14:
Which statement is NOT correct about SVM for a problem with 2 set of input features and a binary class of output?
Group of answer choices
SVM maximizes the margin between support vectors
Hyperplane is the line that separates two classes
SVM is a good approach only for smaller datasets
Support vectors are the features that are closer to the hyperplane
Question 15:
A scatter plot of two features for predicting three classes is shown below. What is the best machine learning model that we can fit on this information?
Linear regression
It is impossible to make any classification on this data even with low accuracy
SVM with RBF kernel
Linear SVM
