May 1, 2024
4 minute read
Model assessment is the process of evaluating the performance of a model on new data. It is an important part of the modeling process, as it allows us to determine how well the model is likely to perform in practice. There are many different ways to assess a model, and the best approach will vary depending on the type of model and the data available.
Types of Model Assessment
There are two main types of model assessment: internal assessment and external assessment.
Internal assessment is conducted using the same data that was used to train the model. This type of assessment can be used to identify problems with the model, such as overfitting or underfitting. Overfitting occurs when the model is too complex and learns the training data too well, leading to poor performance on new data. Underfitting occurs when the model is too simple and does not learn the training data well enough, leading to poor performance on both training and new data.
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Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Model Assessment.
Classic in the field of machine learning and provides a comprehensive overview of statistical learning methods, including model assessment techniques.
Provides a comprehensive overview of information theory, inference, and learning algorithms, including a discussion of model assessment techniques.
Provides a statistical perspective on machine learning, including a discussion of model assessment techniques.
Provides a comprehensive overview of reinforcement learning techniques for healthcare applications, including a discussion of model assessment techniques.
Provides a comprehensive overview of natural language processing with transformers, including a discussion of model assessment techniques.
Provides a comprehensive overview of generative adversarial networks, including a discussion of model assessment techniques.
Provides a Bayesian and optimization perspective on machine learning, including a discussion of model assessment techniques.
Practical guide to machine learning using the Scikit-Learn and TensorFlow libraries, including a discussion of model assessment techniques.
Practical guide to deep learning using the fastai and PyTorch libraries, including a discussion of model assessment techniques.
Practical guide to interpretable machine learning, including a discussion of model assessment techniques.
Practical guide to machine learning, including a discussion of model assessment techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/pceulq/model
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