Fall 23 Review

Fall 23 Review

12th Grade

8 Qs

quiz-placeholder

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Fall 23 Review

Fall 23 Review

Assessment

Quiz

Computers

12th Grade

Hard

Created by

ACM UCLA

Used 5+ times

FREE Resource

8 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

  1. What is not supervised learning?

  1. Training a model with labeled data -- giving it the expected answers!

Grouping people into friend groups based on data about their favorite movies!

Predicting the price of a house given historical data about cost of houses with known square footage.

  1. Classifying cats and dogs, given pictures of each with labels.

2.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

Which of the following is true? [2 correct]

  1. Classification problems have a continuous output line

Classification predicts classes or categories

An example of regression is seeing if a picture is a cat or dog

Regression involves predicting the continuous relationship between input features and the output.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

  1. What kind of training data should you provide a supervised learning model for best results?

  1. Give the model one specific type of example data only.

  1. Give the model a lot of diverse data.

  1. Give the model unlabeled data

  1. Give the model one or two examples only.

4.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

Media Image

What is linear regression?

  1. The process of shifting weights and biases to minimize loss

Moving to the left along a line to optimize the learning rate.

Approximating a continuous (linear) output; finding the relationship between X and y

"Finding the line of best fit"

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is loss?

A number indicating how bad the model’s prediction was on a single example.

  1. A function that transforms any number into a probability

A line that shows the relationship between input features and output

  1. A line that separates classes

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is gradient descent important?

  1. We want to make our predictions smaller, so descending helps

We use it to change the weights / biases to make our model more accurate

  1. Gradient descent increases loss. More loss helps our model get close to 0

We use it to prevent the model from overfitting the data.

7.

MULTIPLE SELECT QUESTION

45 sec • 1 pt

What is mean squared error? 2 correct answers!

The absolute difference between actual value and predicted value

Media Image

A loss function

The gradient of a function

8.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

  1. What do we call the line that separates two classes in logistic regression?

Decision boundary

Loss function

Line of best fit