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WorksheetsData Mining 2.1
Total questions: 14
Worksheet time: 22mins
From your previous understanding, what one or two words best describe Machine Learning?
What best captures the goal of machine learning?
Building systems that automatically learn and improve from experience
Designing algorithms that follow pre-set instructions
Building software that performs repetitive computations efficiently
Writing code that minimizes human supervision
What makes ML different from traditional programming?
It writes its own rules
It needs fewer computations
It avoids using data
It runs on specialized hardware
Which of the following tasks would least likely involve machine learning?
Grouping similar customers based on purchase history
Filtering unwanted emails from an inbox
Sorting documents alphabetically by filename
Suggesting words while composing a text message
Who first popularized the term “Machine Learning”?
Alan Turing
Arthur Samuel
Ada Lovelace
Marvin Minsky
How are Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) related?
AI is a subset of ML, and ML is a subset of DL
ML is a subset of AI, and DL is a subset of ML
DL is separate from ML and AI
AI and ML refer to the same discipline
Identify the type of Machine Learning.
Match the following components of Machine Learning.
Regression
Predicting continuous outcomes
Classification
Categorizing data into classes
Clustering
Grouping similar data points
Neural Networks
Modeling complex relationships
Organize these options into the right categories
Support Vector Machine
Match each supervised learning model to the mathematical concept that best describes how it works.
Linear Regression
Weighted sum of features
Logistic Regression
Probability estimation using a logistic function
Decision Trees
Recursive partitioning of data
Support Vector Machines
Maximizing the margin between classes
Neural Networks
Layered structure of interconnected nodes
The dataset below shows a portion of the Iris dataset used for classification tasks.
In a typical machine learning workflow, the dataset is divided into several subsets.
What do these subsets each represent?
Arrange the steps in evaluating a supervised learning model in the correct logical sequence.
Split the dataset into training and testing sets
Fit the model using the training data
Generate predictions on the test data
Compute performance metrics
Interpret and compare model results
Two models were tested on the same dataset.
Model A: Accuracy = 92%, F1-Score = 0.71
Model B: Accuracy = 89%, F1-Score = 0.83
Which model would you prefer for an imbalanced dataset, and why?
A bank wants to develop a model to detect fraudulent credit card transactions.
Fraud cases are rare compared to normal transactions, but missing them can cause financial loss.
Question: Which metric or combination of metrics should you focus on, and why?
