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Determine the correct data partition for tuning a model's parameters and features.
Case context: An engineering team is building a predictive algorithm. They have a full dataset and need to decide which data columns to use as inputs (features) and what specific configuration settings to apply to their algorithm (parameters).
Question: Which specific data partition should the team evaluate their algorithm on to make these choices, and what is another name for this partition?
Sample answer: The team should use the development set, which is also sometimes called the hold-out cross validation set, to tune their parameters and select their features.
Key points:
- Identifies the development set as the correct partition.
- Provides the alternative name: hold-out cross validation set.
- Connects the team's need to select features and tune parameters directly to the dev set's stated purpose.
Rubric: The answer should correctly identify the development set and its alternative name as the proper tool for the team's parameter and feature selection tasks.
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Related
Purpose of Dev and Test Sets
Choosing Dev and Test Sets to Reflect Future Data
Choosing Dev and Test Sets from the Same Distribution When Possible
Changing Dev/Test Sets or Evaluation Metrics During a Project Is Common
Quickly Establishing Dev/Test Sets and Metric for New Applications
Sizing the Dev Set to Detect Meaningful Accuracy Changes
What is the primary purpose of a development (dev) set in a machine learning project?
The development set is sometimes called the 'hold-out cross validation set'.
The development set is sometimes also called the _____ cross validation set.
Match each development set role to its description in Machine Learning Yearning.
Order the steps for using a dev set to choose between two model configurations.
Which of the following is NOT a stated use of the development set in Machine Learning Yearning?
The dev set and the training set refer to the same data partition in Machine Learning Yearning.
The dev set is used to tune parameters, _____ features, and make other decisions about the learning algorithm.
Match each dev set use from Machine Learning Yearning to the activity that exemplifies it.
Arrange the stages of a machine learning project that involve the dev set in the correct workflow order.
Explain the role and alternative terminology for the development set.
Determine the correct data partition for tuning a model's parameters and features.
List the three primary uses of the development set.