ML U1

Which problem can a single Decision Tree often suffer from if it’s too tall

The probability of waking up for an 8 a.m. lecture is:

Trying out different hairstyles for a wedding and picking the best based on friends' votes is:

Receiving an attendance message for a class you didn’t attend is an example of:

You split your exam syllabus into easy, medium, and tough topics before studying. In ML, this is like:

Which one is NOT a Supervised Learning algorithm?

Which is typically the first step in a machine learning process?

Grouping animals based on their looks without knowing their species is an example of:

What is the purpose of cross-validation in machine learning?

In the bias-variance tradeoff, what is the effect of increasing model complexity?

Which of the following techniques is commonly used to test the performance of a machine learning model?

Choosing a movie based on both genre and rating is similar to:

Which technique is primarily used to find the best combination of hyperparameters for a machine learning model?

When you split data into folds, it’s called:

Which of these algorithms is famous for separating spam emails?

Netflix recommending a new show after you binge a series is most like:

A model that fits the training data perfectly but fails badly on new data is said to be:

If you can’t decide what to eat, and you ask 10 of your friends and go with the majority vote, you’re basically using:

Picking the fastest route to college using Google Maps is like a:

Finding a balance between watching Netflix and studying before exams is like balancing:

When Instagram recommends reels similar to what you binge-watched last night, it’s:

Which step comes after training a model?

Reinforcement Learning describes?

A bank wants 2 categorize its customers in different groups based on income spending habits without predefined labels.

If a system learns by trial-and-error like you figuring out fastest way 2 sneak snacks into class without being caught

A model separating cats and dogs by drawing a line is:

Which of these is closest to how a baby learns to walk by falling and getting up again?

Choosing the best combination of shoes, shirt, and sunglasses for a college trip is like:

If each time u make a mistake in a test, u improve by fixing biggest error first & slowly reduce mistakes, you’re using:

Guessing a mystery number and being told “higher” or “lower” is like:

If your friend keeps changing their decision every time you ask them which movie to watch, it’s like a model with:

When you decide what to eat by asking yourself: “Am I hungry? Yes → Pizza, No → Ice cream,” you’re following:

Ordering food based on your previous orders being predicted by Swiggy is:

If your pizza delivery time increases with your distance from the restaurant, this relationship can be modeled using:

Before choosing a dress for a college fest, u check what's trending online & in ur wardrobe. In ML terms, you’re doing

When your phone’s gallery automatically groups your photos into categories like “Selfies,” “Screenshots,” and “Food,”

Splitting your group project into subgroups based on strengths is:

Guessing your exam grade before results based on how well you attempted is like:

In which type of machine learning does the model learn from labeled data?

When you revise a topic multiple times using different study methods to see what works best, it’s like:

algorithm which overprotective friend giving u advice based on all their past experiences, reducing the chance u mess up

Which split ratio is commonly used for training and testing?

Tuning hyperparameters means:

A student who memorizes answers without understanding might score well on practice papers but fail the actual test.

Your playlist groups songs by genre without asking you. That’s:

What is the probability of getting a head when tossing a fair coin?

Which of these is a popular ML library in Python?

An AI playing chess and learning from wins and losses uses:

A model with high bias is likely to:

A company wants 2 predict cust churn based on past txn data where each cust is labeled as either “churn” or “retain.”