AI Today Podcast: AI Glossary Series – Prediction, Inference, and Generalization
Jan 27, 2023
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Hosts Kathleen Walch and Ron Schemlzer discuss the importance of understanding Prediction, Inference, and Generalization in AI. They explore how models make predictions, the significance of generalization for handling new data, and the interconnected concepts in machine learning.
Prediction involves making predictions based on prior examples in machine learning systems.
Generalization focuses on a model's ability to generate accurate predictions on new data after training.
Deep dives
Prediction in Machine Learning
Prediction in machine learning involves making predictions about data, such as classification or regression tasks, based on prior examples provided to the machine learning system. The purpose of machine learning is to teach systems to make accurate predictions using algorithms that create models for new data.
Generalization in Machine Learning
Generalization in machine learning refers to a model's ability to generate accurate predictions on new data after training. It focuses on the model's performance in general on tasks it has been trained on, including metrics like generalization performance and generalization error, which assess the model's effectiveness on unseen data.
Inference and Machine Learning
Inference in machine learning is the application of trained models to real-world data for tasks the models have been trained on, involving making predictions and generalizations based on prior learning. Inference is the practical implementation of prediction and generalization in real-world scenarios, reflecting the core functions of machine learning systems.
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schemlzer define and discuss at a high level the terms Prediction, Inference, and Generalization, why it’s important to understand these terms, and how they fit into the overall picture of AI.
Show Notes:
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AI Glossary
Glossary Series: Machine Learning, Algorithm, Model