Unlocking Machine Learning: Mastering Features, Labels, and Instances Quiz

1. Identifying Features by Example

   In a dataset where each row describes a house with attributes such as number of bedrooms, square footage, and price, which element represents a feature?

   1. A. Number of bedrooms
   2. B. Price label
   3. C. Training set
   4. D. Instance row
   5. E. Feature set

2. Distinguishing Labels from Features

   If a machine learning model predicts whether an email is spam or not based on word frequencies and sender address, what is the label in this problem?

   1. A. Word frequency
   2. B. Sender address
   3. C. Spam or not spam
   4. D. Email instance
   5. E. Lable

3. Understanding the Term 'Instance'

   In a table of flower measurements (sepal length, petal width, and species type), what does a single row in the table represent?

   1. A. Dataset
   2. B. Instance
   3. C. Label vector
   4. D. Feature mapping
   5. E. Featur

4. Features in Structured and Unstructured Data

   When analyzing a collection of digital images for object recognition, which of the following most accurately describes a feature?

   1. A. The label assigned to an image such as 'cat' or 'dog'
   2. B. Pixel intensity values extracted from each image
   3. C. The directory containing all the image files
   4. D. The number of objects predicted
   5. E. Lable

5. Feature, Label, and Instance Relationships

   Which statement best describes the relationship among features, instances, and labels in supervised learning?

   1. A. Each feature represents an output to be predicted for the entire dataset.
   2. B. An instance is a collection of labels for one feature in the dataset.
   3. C. Features are the input variables for each instance, labels are the outcomes, and each row in the dataset is an instance.
   4. D. Labels and features are interchangeable terms for any machine learning variable.
   5. E. An instance is a collection of features for all classes in the label set.