What is the term for the process of fine-tuning a machine learning model?
Model tuning
Explanation
Model tuning (also called hyperparameter tuning) is the process of adjusting a machine learning model’s settings to improve its performance, accuracy, and generalization.
Examples of parameters that may be tuned include:
Learning rate
Number of trees (for decision trees/random forests)
Maximum tree depth
Batch size
Number of hidden layers (for neural networks)
Option Review
a) Model deployment – Making the trained model available for real-world use.
b) Model inference – Using a trained model to make predictions.
c) Model tuning – Fine-tuning or optimizing a model’s parameters to improve performance.
d) Model training – The initial process of learning from data; tuning is typically performed after or alongside training to optimize results.
Model tuning because it accurately describes the process of optimizing a machine learning model by adjusting its parameters and hyperparameters to improve performance. This aligns with the learning objective of understanding key concepts in model development and refinement in machine learning.
