Learn how hyperparameters control AI model training and why mastering them is essential for optimizing performance in machine learning projects.
Table of Contents
Question
Which of the following entities are used by a data scientist to control the model training process?
A. Statistical analyses
B. Hyperparameters
C. Encryption keys
D. Business objectives
Answer
B. Hyperparameters
Explanation
A data scientist uses hyperparameters to control the model training process. Hyperparameters are adjustable parameters that determine how a machine learning model learns from data. They are not directly learned by the model itself but are set beforehand by the data scientist. Examples include regularization strength, learning rate, and the number of layers that are hidden in a neural network. Adjusting these parameters can significantly impact the performance of the model.
Business objectives guide the overall direction of the project but do not directly control the training process itself.
Encryption keys are used for data security and privacy, not for controlling the model training process.
Statistical analyses are used to analyze data and extract insights, but they are not directly involved in controlling the model training process.
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