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AIML_Quiz
AIML_Quiz
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1
What is classification in supervised learning?
When The AI can make predictions on the pricing of a house.
When the AI can classify certain things into categories
When the AI can find the relationship between two variables
2
Informed search algorithms are called Heuristic search
True
False
3
K-NN is the best algorithm for classification in supervised learning.
True
False
4
What is the difference between simple linear regression and multiple linear regression?
In simple regression, p = 1
In simple regression there are multiple variables
In multiple regression there is only one variable
5
A hypotheis is always true.
False
True
6
What is the primary purpose of the 'Random Forest' algorithm in machine learning?
To recursively generate numbers based on a particular rule given as input
To encrypt data for cybersecurity
To classify data or predict outcomes based on input variables
To create digital forests in simulations
7
Which algorithm is commonly used for clustering in unsupervised learning?
Random Forest
Support Vector Machines
Logistic Regression
K-Means
8
Association rule mining is a/an ___ machine learning algorithm
supervised
unsupervised
9
Which of the following is a commonly used algorithm in machine learning?
Blockchain
Quantum Computing
Linear Regression
Relational Database Management
10
What is an
Association
in Machine Learning?
identified relationships between various chosen items
self-contained step-by-step operations to be performed
computer models that separate data into distinct classes
data instances grouped together, maximizing similarity
11
Uninformed search algorithms are called Blind search
True
False
12
Which of the following is not considered as an algorithm in Machine Learning?
LISP
Nearest Neighbor
Neural Networks
Decision Trees
13
Descriptive and Inferencial statistics are the same thing.
False
True
14
When to use the k-mean algorithm in Machine learning ?
Classification
Clustering
Labeling
Regression
15
In inferencial statistics, parameter is
a statistic of the sample
a parameter of a statistical function
a characteristic of the population
a characteristic of a sample
16
What is the main difference between regression and classification in supervised learning?
Regression predicts continuous numbers, classification predicts categories
Regression predicts categories, classification predicts continuous numbers
Regression uses unlabeled data, classification uses labeled data
Both terms mean the same thing
17
What is a linear regression model?
exponential equation that models the relationship b/w data
a linear equation that models the relationship between data
y=abx
y=ax+b
18
Which of these uninformed search algorithms explores the shallowest nodes first?
Breadth-First Search
Depth-First Search
Iterative Deepening Search
Uniform-Cost Search
19
Standard deviation is a measure of:
the centre
the outliers
bad behaviour
the spread
20
What is clustering in unsupervised learning?
Predicting a continuous output variable
Training a model with labeled examples
Grouping similar data points based on features
Assigning labels to input data
21
What is the purpose of a decision tree in ML?
B. Store raw data
C. Enhance data visualization
D. Predict the future
A. Make decisions based on data
22
A government agency that supervises the stock market and punishes fraudulent trading practices
FDIC
HOLC
SEC
FHA
23
What is the difference between a model and an algorithm in machine learning?
Algorithm:specific set of rules; Model: an instance of an algorithm.
There is no difference; the terms are used interchangeably.
A model is used for training, and an algorithm is used for testing.
An algorithm is a neural network, and a model is a decision tree.
24
Linear Regression
110
75
91
96
25
Where is decision tree used in?
Clustering problem
Dimensionality Reduction
Regression problem
Classification problem
26
Instead of writing code, we feed data to the generic algorithm in Machine learning
False
True
27
Standard deviation is an example of what?
Measure of dispersion
Interval data
Measure of central tendency
Order effect
28
What is the main characteristic of clustering in unsupervised learning?
Clustering automatically groups data points based on similarities.
Clustering is only used for textual data.
Clustering assigns data points to pre-defined categories.
Clustering requires manually labeling each data point.
29
Decision tree analysis helps in:
None of the above
Estimating supply-demand curves
Understanding market trends
Calculating the expected value of uncertain outcomes
30
Pruning a decision tree results in a smaller decision tree
False
True
31
What is Association Rule Mining in machine learning?
A process to reduce the dimensionality of data.
An approach to predict continuous numerical values.
A technique to find patterns in a dataset that occur together frequently.
A method to classify data into predefined categories.
32
What does a branch represent in a decision tree?
Value of an attribute
Decision makers
Test of an attribute
A category in the classification
33
What is the primary goal of the Gradient Descent algorithm in machine learning?
To calculate probabilities for classification
To maximize the variance of the data
To minimize a cost or loss function
34
What is the primary objective of clustering in Unsupervised Learning?
Reducing dimensionality
Grouping similar data points together
Maximising rewards
Predicting a target variable
35
What are rules used for in machine learning?
So it knows what can't be in the image
For outliers
So it can't go out of its boundaries
So it can identify the image
36
In a decision tree Net Gain =
Expected value - initial cost of decision
Expected value x initial cost of decision
Initial cost of decision - expected value
Number of issues shared x Current share price
37
Machine learning means learning from
data.
books.
cars.
machines.
38
In association rule learning, what does support refer to?
The number of transactions containing both items in the rule
The probability of a rule being true
The confidence level of the rule
The number of transactions in the dataset
39
Breadth-first search (BFS) is one of the uninformed search algorithms.
False
True
40
Informed search algorithms use:
No heuristics
Random search strategies
Inadmissible heuristics
Admissible heuristics
41
Inferencial statistics is :
There is no such study of statistics
All options are correct
summarizing your data; describing your sample.
using your sample data to infer some conclusions about your population
42
K-nearest Neighbor is a parametric algorithm in machine learning.
True
False
43
What is descriptive statistics?
Explaining the Data
Collecting the Data
Interpreting the Data
Describing the Data
44
Which loss is typically used for classification in supervised learning?
Mean squared error loss
Huber loss
Focal loss
Categorical cross-entropy loss
45
What is the key difference between informed and uninformed search algorithms?
Uninformed search requires more memory
Informed search avoids the use of cost functions
Informed search uses heuristics to guide the search
46
What is another word for hypotheisis
Evaluation
Question
Prediction
Answer
47
With Inferencial Statistics we describe the features of data
True
False
48
What is the purpose of the support vector machine (SVM) algorithm in machine learning?
Classify data: Find best hyperplane to separate categories
To perform feature selection in high-dimensional datasets
To handle missing values in the dataset
To cluster similar data points
49
What is a hypotheis?
the list of things the scientist needs to experiment
a prediction a scientist makes about what will happen
something that is changed in an experiment
the steps a scientist follows to perform an experiment
50
Which statement about uninformed search algorithms is true?
Breadth-first search has better time complexity than depth-first search.
Depth-first search has better space complexity than breadth-first search.
None of those.
Depth-first search terminates on all state spaces.
