WorksheetsML Assessment 3
Total questions: 81
Worksheet time: 41mins
Identify the Problem:
We have Information of all previous general elections with respect to party, votes, Candidates, Locality, Issues in nation, etc. With All this information we need to find the winner of next general election.
Classification
Regression
Clustering
none of them
Identify the problem:
We have information of all houses in a particular area like size, type and design of house, locality, colour, previous purchase etc. on the basis of these data we have to find the price of a particular house.
Classification
Regression
Clustering
Prbabilistic
Identify a problem:
You have details of corona affected positive cases found in various regions. Depending upon parameters like number of increasing corona infected patients, weather conditions etc. regions are classified into red zone, Green zone and Orange Zone. On the basis of these data we have to predict the region of a city.
classification
regression
clustering
unsupervised learning
State whether True or False
The most meaningful and interpretable principal components are those that have the largest eigenvalues
True
False
Cross validation is used for
Comparing predictors
Selecting parameters in prediction function
Selecting variables to include in a model
All of the mentioned
State True or False
For k cross-validation, larger k value implies more bias.
True
False
which of the following is not a method of cross validation?
Leave One Out CV
K-Fold CV
Stratified K-Fold CV
Timeline CV
If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
Overfitting
Underfitting
Bestfitting
Give the correct Answer for following statements.
1. It is important to perform feature normalization before using the Gaussian kernel.
2. The maximum value of the Gaussian kernel (i.e., ) is 1.
Statement 1 is True and 2 is False
Statement 1 is False and 2 is True
Both statements are False
Both Statements are True
Which is/are method to do multiclass classification?
One Vs Rest
One vs One
All vs One
One vs Other
Following is Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.
If you remove the following any one red points from the data. Will the decision boundary change?
True
False
Suppose you are dealing with 4 class classification problem and you want to train a SVM model on the data for that you are using One-vs-all method. Now, say for training 1 time in one vs all setting the SVM is taking 10 second. How many seconds would it require to train one-vs-all method end to end?
20
40
80
60
For evaluating regression models, which of the following metrics can not be used ?
R Squared
Adjusted R Squared
SSE
MST
Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression.You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.
Which of the following is true for X1?
Relation between the X1 and Y is weak
Relation between the X1 and Y is strong
Relation between the X1 and Y is nutral
Correlation can’t judge the relationship
Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.
What will happen when you fit degree 4 polynomial in linear regression?
There are high chances that degree 4 polynomial will over fit the data
There are high chances that degree 4 polynomial will under fit the data
Can't Predict
None of these
Which of the following function is used by Logistic Regression to convert the probability in between [0,1]
Sigmoid
polynomial
square
rbf
Regarding Bias and Variance ,which of the following statement is True?
Model which overfit has high bias and high variance
Model which overfits have Low bias and low variance
Model which overfits has high Bias and Low variance
Model which overfits has low Bias and High Variance
Which of the following is true about Lasso and Ridge Regression?
Ridge regression uses subset selection of features
Lasso regression uses subset selection of features
Both uses subset selection of features
None of them are used for subset selection of features
Identify Performance measure P from following Traffic Pattern Analysis Algorithm.
Predict traffic patterns at a busy intersection
Tune Model with data about past traffic patterns
Predict future traffic patterns
None of the above
Select All prime reasons for using unsupervised Machine Learning Algorithms.
Finds all kind of unknown patterns in data.
Help you to find features which can be useful for categorization.
Help you to Predict whether a fruit is apple or not
Help you to predict prize of stock
What is Machine Learning? (Choose 3 Answers)
Artificial Intelligence
Machine Learning
Data Statistics
Deep Learning
from the picture, what kind of programming is it?
Traditional Programming
Machine Learning
Modern Programming
Traditional Learning
What kind of learning algorithm for "Future stock prices or currency exchange rates"?
Recognizing Anomalies
Prediction
Generating Patterns
Recognition Patterns
Which of the following is not type of learning?
Semi-unsupervised Learning
Unsupervised Learning
Supervised Learning
Reinforcement Learning
Real-Time decisions, Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are applications in ...
Unsupervised Learning: Clustering
Supervised Learning: Classification
Reinforcement Learning
Unsupervised Learning: Regression
Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in ...
Unsupervised Learning: Clustering
Supervised Learning: Classification
Reinforcement Learning
Unsupervised Learning: Regression
This picture shows a result of ...
Supervised Learning: Classification
Unsupervised Learning: Regression
Unsupervised Learning: Prediction
Supervised Learning: Regression
__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.
Artificial Intelligence
Machine Learning
Deep Learning
Traditional Learning
What device below is not an example of Machine Learning?
Wearable fitness tracker
Google Assistant
Speech to Text
Google Search
None of the above
What are the three types of Machine Learning? Choose three.
Supervised Learning
Learning Differentiated
Unsupervised Learning
Reinforcement Learning
Technical Learning
What are the two types of Supervised Learning?
Classification
Declassification
Progression
Regression
What are the two types of Unsupervised Learning?
Loitering
Clustering
Association
Dissociation
In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Technique Learning
What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses?
Unsupervised
Reinforcement
Supervised
Deep Learning
What kind of learning algorithm for "Facial identities or facial expressions"?
Recognizing Anomalies
Prediction
Generating Patterns
Recognition Patterns
ML is a field of AI consisting of learning algorithms that?
Improve their performance
At executing some task
Over time with experience
All of the above
Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?
Classifying emails as spam or not spam
Watching you label emails as spam or not spam
The number of emails correctly classified as spam/not spam
None of the above
Labeled Data are used in _______ Machine Learning algorithm
Supervised
Unsupervised
Unlabeled Data are used in _______ Machine Learning algorithm
Supervised
Unsupervised
Machine learning is mostly used when
Human expertise doesn't exist
Model must be customised to personal need
Model use huge amount of data
Interpreting the insight from descriptive data such as mean, median etc
Google Translate uses ________________ to improve its results.
Machine Learning
Internet
Machine Optimization
Data Warehouses
What type of Machine Learning Algorithm is suitable for predicting the continuous dependent variable?
Logistic Regression
Linear Regression
Decision Tree Classifier
KNN Classifier
What type of Machine Learning Algorithm is suitable for predicting the dependent variable with two different values?
Logistic Regression
Linear Regression
Multiple Linear Regression
Polynomial Regression
The correlation in between mobile usage and exam score of a person found to be -2.2. What is your inference from the above statement.
Mobile usage is positively correlated with exam score
Mobile usage is negatively correlated with exam score
None of the mentioned
Need some other information
The residual is the difference in between ________________
actual value of y and the estimated value of y
actual value of x and the estimated value of x
actual value of y and the estimated value of x
actual value of x and the estimated value of y
Suitable evaluation metric for measuring the performance of a given regression model is
Mean Absolute Error
Root Mean Square Error
Precision
Recall
If we decrease the input variable by one unit in a simple linear regression model. How many units of the output variable will change?
reduced by Intercept
increased by Intercept
increased by Slope
reduced by Slope
Appropriate chart for visualizing the linear relationship between two variables is _________________
Scatter plot
Barchart
Histograms
None of Mentioned
The Number of coefficients required to estimate a simple linear regression?
1
2
0
3
KNN Algorithm can be used for
Only for Classification
Only for Regression
Both Classification and Regression
None of the Mentioned
KNN is ___________ algorithm
Non-parametric and Lazy Learning
Parametric and Lazy Learning
Parametric and Eager Learning
Non-parametric and Eager Learning
What kind of distance metric(s) are suitable for categorical variables to finding the closest neighbors
Euclidean Distance
Manhattan distance
Minkowski distance
Hamming distance
What kind of distance metric(s) are suitable for continuous variables to find the closest neighbors
Euclidean Distance
Manhattan distance
Minkowski distance
Hamming distance
KNN algorithm appropriate for
Lower number of features
Large number of features
No such restriction on number of features
None of the Mentioned
KNN algorithm requires
More time for training
More time for testing
Equal time for training and testing
None of the Mentioned
The entropy of a given dataset is zero. This statement implies what?
further splitting is required
no further splitting is required
Need some other information to decide splitting
None of the Mentioned
If the given dataset contains 100 observations out of 50 belongs to class1 and other 50 belongs to class2. What will be the entropy of the given dataset?
0
1
-1
0.5
How do you choose the root node while constructing a Decision Tree?
An attribute having high entropy
An attribute having largest information gain
An attribute having high entropy and Information gain
None of the Mentioned
Chose the correct criterion for Decision Tree Classifier in sklearn package
Gini
Entropy
Information Gain
Random
In a Decision Tree Leaf Node represents_____________
One of the Class Label
One of the complete observation
One of the attribute
None of the Mentioned
Consider the above Confusion Matrix of a classifier and choose the correct statements
Accuracy is 84%
Misclassification Rate is 16%
Type-I Error is 6
Type-II Error is 10
In feature scaling, what is the purpose of normalization?
To convert data into a standard normal distribution
To make all features have the same scale
To remove outliers from the data
To reduce the dimensionality of the data
Among the following option identify the one which is not a type of learning
Semi Unsupervised Learning
Supervised Learning
Reinforcement Learning
Unsupervised Learning
In Exploratory Data Analysis (EDA), what does a histogram visualize?
Correlation between variables
Distribution of a single variable
Relationships between categorical variables
Outliers in the dataset
What is the purpose of encoding categorical variables in machine learning?
To add noise to the dataset
To convert numerical variables into categorical ones
To handle categorical data so that it can be used as input for machine learning models
To remove outliers from categorical variables
What is Machine Learning?
A computer program that learns from data
A set of rules and instructions for a computer
A type of computer hardware
A way to store and retrieve information
What is the primary goal of data cleaning in the context of machine learning?
To remove outliers from the dataset
To make the dataset more complex
To convert categorical variables into numerical ones
To ensure data quality and accuracy
What is the role of outlier detection in data preprocessing?
To remove all data points in the dataset
To identify and handle extreme values that can negatively impact model performance
To create synthetic outliers for model training
To convert numerical variables into categorical ones
What is the purpose of a box plot in exploratory data analysis?
To visualize the distribution of a variable
To identify outliers in the dataset
To measure the correlation between two variables
To encode categorical variables into numerical format
What is the primary goal of unsupervised learning?
Classification
Regression
Clustering
Reinforcement learning
What is the purpose of handling missing data in a dataset?
To remove the entire row with missing data
To replace missing values with the mean or median of the variable
To ignore missing data during model training
To create synthetic data for missing values
In k-mean algorithm, K stands for
Number of data
Number of clusters
Number of attributes
Number of iterations
Can Decision Tree be used for clustering?
Yes
No
Which clustering method takes care of variance in data?
k-mean
Gaussian mixture model
Decision tree
All of the answers
Which clustering method takes care of oblong dataset?
k-mean
Gaussian mixture model
Decision tree
All of the answers
Does k-mean algorithm always converge?
Yes
No
Is Gaussian mixture model supervised?
Yes, It is supervised
No, it is unsupervised.
Is k-mean clustering supervised?
Yes, It is supervised
No, it is unsupervised.
Is Gaussian mixture model Probabilistic?
Yes, It is probabilistic.
No, it is not probabilistic.
In Gaussian mixture model clustering, the number of Gaussian distribution functions used is equal to
Number of clusters
Number of attributes
Number of instances
Number of iterations
Which among the following is not true for a Bayesian classifier?
A (natural) class is to predict the values of features for members of that class.
It is a probabilistic model.
It is based on Bayes' theorem.
It is not used in data mining.
