Question

Which of the following principle(s) is/are True for gathering your data samples? (1 point)

1. Include enough labeled examples in each category.
2. Capture only a limited range of variation in your problem space.
3. Match data to the intended output for your model

Answer 1

The principles for gathering data samples include including enough labeled examples, capturing a limited range of variation, and matching the data to the intended output of the model.

Step-by-step explanation:

The following principles are True for gathering data samples:

1. Include enough labeled examples in each category.
2. Capture only a limited range of variation in your problem space.
3. Match data to the intended output for your model.

For example, when training a machine learning model to classify images of cats and dogs, you would need to include a sufficient number of labeled examples for both categories to ensure the model can learn the distinguishing features between cats and dogs. Additionally, you would want to capture various breeds and poses to cover the limited range of variation in the problem space. Finally, you would need to ensure that the data you collect aligns with the expected output of the model, such as labeling cat images as 'cat' and dog images as 'dog'.

Learn more about data gathering for machine learning models