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WorksheetsML chapter 1
Total questions: 50
Worksheet time: 8mins
in finance, machine learning is used to study consumer behhaviour.
True
False
in finance, learning models are used for optimization, control, and troubleshooting.
True
False
in telecommunications, call patterns are analyed for network optimization and maximizing the quality of service.
True
False
in............, machine learning is used to study consumer behavior.
finance
telecommunications
retail business
medicine
in............, learning programs are used for medical diagnosis.
finance
telecommunications
retail business
medicine
Which of the following is NOT a type of supervised learning task?
Classification (predicting a category)
Regression
(predicting a continuous value)
Dimensionality reduction
(reducing the number of features)
Clustering
(grouping data points without labels)
In supervised learning, the data used to train the model is called:
Test data
Validation data
Training data
Raw data
The main goal of supervised learning is to:
Find hidden patterns in unlabeled data
Learn from labeled data to make predictions for new data
Simplify complex data for visualization
Reduce the computational cost of processing data
What does ordinal data denote?
A nominal variable with categories falling in an unordered list.
A nominal variable with categories falling in an ordered list
A feature that represents a characteristic measured in numbers.
A feature that can take on one of a limited, and usually fixed, number of possible values.
A common example of a supervised learning algorithm is:
K-Means clustering
Principal Component Analysis (PCA)
Support Vector Machine (SVM)
When evaluating the performance of a supervised learning model, we often use metrics like:
Accuracy
(for classification tasks)
Mean Squared Error (MSE)
Both A & B
None of these
Supervised learning models are most effective when the training data is:
Large and diverse
Small and specific
Unstructured and messy
It doesn't matter
(as long as it's well-labeled and representative)
Learning is a_____and_____process.
simple , individual
simple , individual
complex , active
complex , active
What the child learns, how well she learns it and how she is able to apply that learning to various
situations depends on:
Opportunities she has to observe and interact with objects and people
Opportunities the teacher provides for memorization and recall
The kind of punishment she is given to ensure learning
The variety of materialistic rewards she is offered to complete the tasks
In machine learning,a...... is the problem of predicting the value of a
numeric vari-able based on observed values of the variable.
classification
regression
clustering
none of these
The equation for Simple linear regression is:
y=a + bx
y=a0 + a1x1 + + anxn
y=a0 + a1x + + anxn
none of these
in supervised learning regression the...... There are more than one independent variable.
polynomial regression
simple linear regression
multivariate linear regression
logistic regression
in supervised learning regression a...... dependent variable is binary, that is, a variable which takes only the values 0 and 1.
polynomial regression
simple linear regression
multivariate linear regression
logistic regression
in Polynomial regression There is only one continuous independent variable x and the assumed model is:
y=a + bx
y=a0 + a1x1 + + anxn
y=a0 + a1x + + anxn
None of these
is regression related to the machine learning.
True
False
Human stored data in ......and computer stored data in....?
brain , cpu
brain , hard disk drives
eye , flash memory
eye , cpu
Abstraction is a?
The term generalization describes the process of turning the knowledge about stored
data into a form that can be utilized for future action.
It is the process of giving feedback to the user to measure the utility of the learned
knowledge. This feedback is then utilised to effect improvements in the whole
learning process.
The process of fitting a model to a dataset is known as training. When the model
has been trained, the data is transformed into an abstract form that summarizes the
original information.
None of these
What is classification in machine learning?
Identifying colors
Sorting shapes
Categorizing into groups
Counting numbers
What is a real-life example of a classification problem mentioned in the text?
Baking cookies
Painting pictures
Face recognition
Driving cars
Which algorithm is NOT mentioned as a machine learning algorithm for classification?
Logistic regression
Random forest algorithm
Alphabetical algorithm
Decision tree algorithm
How many classes are typically involved in a binary classification problem?
1
3
2
4
What is a problem with more than two classes often called in classification?
Multi-label
Binary
Trinary
Quartinary
Which of the following is an example of a multi-class classification problem?
Sorting fruits into apples and bananas
Categorizing animals into mammals and fish
Determining whether a shape is a circle or square
Identifying different species of flowers
A binary classification problem involves classifying into one class only.
True
False
is unsupervised learning part of machine learning
True
False
is reinforcment learning part of machine learning
True
False
What is the primary goal of unsupervised learning?
Minimize error between predicted and actual outputs
Predict a target variable based on input features
Discover patterns and structures in data
Classify data points into predefined categories
What is the basic principle of reinforcement learning?
Learning from labeled data
Learning from rewards and punishments
Learning from input-output pairs
Learning from supervised signals
Which of the following represents exploration in reinforcement learning?
Selecting actions that are known to yield high rewards
Selecting actions randomly to discover new strategies
Selecting actions that have resulted in negative rewards previously
Selecting actions based on a predefined policy
Which of the following is an example of unsupervised learning algorithm?
Linear Regression
Decision Trees
K-means Clustering
Support Vector Machines
Which of the following tasks is an example of unsupervised learning?
Predicting stock prices
Identifying spam emails
Grouping similar news articles
Recognizing handwritten digits
Which of the following is NOT a component of reinforcement learning?
Agent
Environment
Rewards
Supervised learning algorithm
What is the primary goal of machine learning?
Explicit programming
Automation of repetitive tasks
Improving performance on a task without being explicitly programmed
Creating complex algorithms
How is learning defined in the context of machine learning?
Memorizing information
Absorbing knowledge through experience and data
Following predefined rules
Replicating human thought processes
Which statement best describes the relationship between learning and machine learning?
Learning in machine learning refers only to memorization.
Machine learning involves systems improving performance solely through explicit programming.
Learning in machine learning mirrors the human process, improving performance based on experience and data.
Machine learning doesn't involve learning; it relies solely on predefined algorithms
Association rule learning is a machine learning method for discovering interesting relations, called “association rules”, between variables in large databases using some measures of interestingness
True
False
Is learning associate important for a machine learning
True or false
True
False
There are several algorithms for generating association rules. Some of the well-known algorithms are listed below
Apriori algorithm
Eclat algorithm
FP-Growth Algorithm
All of the above
A unit of observation is the smallest entity with measured properties of interest for a study
True
False
In the context of machine learning, an “example” is a recorded property or a characteristic of examples.
True
False
Numeric data is a feature that represents a characteristic measured in numbers
True
False
Ordinal data denotes a nominal variable with categories falling in an unordered list
True
False
What does a “feature” refer to in machine learning?
An instance of the unit of observation for which properties have been recorded
A recorded property or a characteristic of examples
A type of data
A machine learning model.
In the context of spam email detection, what could be the “examples”?
The words used in the messages
Specific messages.
The unit of observation
The spam filter
What is a categorical feature?
A feature that can take on one of a limited, and usually fixed, number of possible values.
A feature that represents a characteristic measured in numbers.
A feature that denotes a nominal variable with categories falling in an ordered list.
A feature that is used to detect spam emails.
