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When a training set contains on the order of a million labeled examples, a neural network is often a good candidate because it can use the extra data to learn more complex patterns.

Large Labeled Datasets Can Make Neural Networks a Strong Choice

Model choice with a large labeled dataset

Large labeled datasets and neural networks

A neural network is often worth considering when you have _____ labeled examples.

Matching data size with model choice

Choosing a Neural Network for a Large Dataset

Question:
A team has about 750,000 labeled satellite tiles for a land-cover classifier. Write a concise explanation of why a neural network may be a sensible choice here.

Sample answer:
A dataset that large can support training a high-capacity model. Neural networks can use many examples to learn complicated patterns and relationships that simpler models may miss, and the extra data helps limit overfitting. For that reason, a neural network is often a good fit when the training set is very large.

Key points:
- Hundreds of thousands of examples count as a very large dataset.
- High-capacity models benefit from abundant data.
- Large training sets reduce the risk that a complex model will overfit.

Rubric:
A strong answer connects dataset scale to the ability of neural networks to learn complex structure and notes that abundant data makes a complex model more practical.

Why very large datasets can favor neural networks

Case context:
Your team has prepared a cleaned dataset for a new image classification problem. The training set contains exactly 1,000,000 labeled examples.

Question:
What model family would you favor for this project, and why does the dataset size matter?

Sample answer:
I would favor a neural network. With about one million labeled examples, a neural network is often a strong choice because large networks can make good use of very large datasets and usually improve as more labeled data becomes available.

Key points:
- Recommend a neural network.
- Use the dataset size of 1,000,000 labeled examples as the reason.
- Explain that neural networks tend to benefit from large amounts of training data.

Rubric:
The answer must recommend a neural network and connect that recommendation to the presence of one million labeled examples.

Choosing a model for a very large labeled classification task

Question:
If a supervised learning problem offers about one million labeled examples, which family of model is often a strong choice?

Sample answer:
A neural network is often a strong choice for a dataset of that size.

Key points:
- Neural network.

Rubric:
Give credit only when the response explicitly names a neural network.

Model choice when data is very large

When is a neural network often a strong choice?

A training set with one million labeled examples is too small to make a neural network a reasonable choice.

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