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Early stopping means stopping gradient descent early based on dev-set error. It reduces variance but increases bias, behaves much like regularization, and is sometimes called a regularization technique.

Early Stopping to Reduce Variance

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

According to Machine Learning Yearning, on which metric is early stopping based?

True or False: Early stopping reduces variance but increases bias in a trained model.

Early stopping means stopping _____ early, based on dev set error.

Match each concept to its specific role in the early stopping technique.

Order the steps for implementing early stopping during model training.

How do some ML authors classify early stopping, according to Machine Learning Yearning?

True or False: Early stopping halts gradient descent based on training set error, not dev set error.

Early stopping behaves a lot like _____ methods, which is why some authors call it a regularization technique.

Match each description to the concept it best characterizes in the context of early stopping.

Order the reasoning steps for deciding whether to use early stopping to address a model's overfitting.

Question:
Discuss the effect of early stopping on a model's bias and variance, and explain why some authors refer to it as a regularization technique.

Sample answer:
Early stopping halts gradient descent early based on the dev-set error. By preventing the model from perfectly fitting the training data, it reduces variance but inherently increases bias. Because it constrains the learning process, it behaves much like regularization, leading some authors to classify it as a regularization technique.

Key points:
- Halts gradient descent based on dev-set error
- Reduces variance
- Increases bias
- Behaves like a regularization technique

Rubric:
The response must mention that early stopping reduces variance while increasing bias. It should also state that stopping gradient descent early based on dev-set error behaves like regularization.

Analyze the trade-offs of early stopping in gradient descent.

Case context:
You are training a neural network and notice that while the training error continues to decrease, the development set error has started to increase. To address this, you implement a method to halt the optimization algorithm based on the dev-set error.

Question:
Based on Machine Learning Yearning, what technique are you applying, what optimization process is being halted, and what specific trade-off between bias and variance should you expect?

Sample answer:
You are applying early stopping, which means you stop gradient descent early based on the dev-set error. The expected trade-off is that this technique will reduce the model's variance but it will increase the bias.

Key points:
- The technique is early stopping
- It halts gradient descent
- It reduces variance
- It increases bias

Rubric:
The learner must correctly identify the technique as early stopping, note that gradient descent is the process being stopped, and explicitly state that it reduces variance while increasing bias.

Applying early stopping to mitigate model overfitting.

Question:
How does early stopping work during model training, and what is its effect on bias and variance?

Sample answer:
Early stopping works by stopping gradient descent early based on the dev-set error. This approach acts similarly to regularization, reducing the model's variance but increasing its bias.

Key points:
- Stops gradient descent early based on dev-set error
- Reduces variance
- Increases bias

Rubric:
The answer must explain that gradient descent is stopped early based on dev-set error and state that this reduces variance while increasing bias.

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