Explore key examples of overfitting in AI models, including chess-playing algorithms and image classification systems. Learn to identify when models perform well on training data but struggle with new scenarios.
Table of Contents
Question
Which of the following are examples of overfitting.
A. A chess-playing model predicts the right move in a scenario that it has seen during training but performs poorly in a scenario it hasn’t seen before.
B. A chess-playing model predicts the wrong move in a scenario that it has seen during training but performs well in a scenario it hasn’t seen before.
C. A model that classifies animals as cats or not-cats accurately identifies a picture of a cat that was not included in the training set but did not identify a cat that was included in the training set.
D. A model that classifies animals as cats or not-cats accurately identifies a picture of a cat included in a training set but does not identify a new cat that it has not seen.
Answer
A. A chess-playing model predicts the right move in a scenario that it has seen during training but performs poorly in a scenario it hasn’t seen before.
D. A model that classifies animals as cats or not-cats accurately identifies a picture of a cat included in a training set but does not identify a new cat that it has not seen.
Explanation
Let’s break down why these are examples of overfitting and why the other options are not:
A. A chess-playing model predicts the right move in a scenario that it has seen during training but performs poorly in a scenario it hasn’t seen before.
This is a clear example of overfitting. The model has memorized specific scenarios from its training data rather than learning general principles of chess strategy. As a result, it can accurately predict moves in familiar situations but fails to generalize this knowledge to new, unseen scenarios. This behavior indicates that the model has overfit to its training data.
D. A model that classifies animals as cats or not-cats accurately identifies a picture of a cat included in a training set but does not identify a new cat that it has not seen.
This is another example of overfitting. The model has learned to recognize specific cats from its training data but hasn’t developed a general understanding of what makes a cat a cat. It can accurately classify images it has seen before but fails when presented with new, unfamiliar cat images. This indicates that the model has memorized the training data rather than learning the underlying features that define a cat.
Now, let’s examine why the other options are not examples of overfitting:
B. A chess-playing model predicts the wrong move in a scenario that it has seen during training but performs well in a scenario it hasn’t seen before.
This is not overfitting. In fact, this describes a model that has generalized well. It may have made a mistake on a training example, but its ability to perform well on unseen data suggests it has learned broader chess principles rather than merely memorizing specific scenarios.
C. A model that classifies animals as cats or not-cats accurately identifies a picture of a cat that was not included in the training set but did not identify a cat that was included in the training set.
This is also not overfitting. The model’s ability to correctly classify a new, unseen cat image suggests it has learned general features of cats. Its failure on a training image might indicate underfitting or other issues, but not overfitting.
In summary, overfitting occurs when a model performs well on its training data but fails to generalize to new, unseen data. This is precisely what we see in options A and D, where the models excel with familiar inputs but struggle when faced with new scenarios or images.
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