https://github.com/slavikdev/ml-runbook

Collection of solutions for common ML problems
https://github.com/slavikdev/ml-runbook

ai cheatsheet machine-learning ml runbook

Last synced: 2 months ago
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Collection of solutions for common ML problems

• Host: GitHub
• URL: https://github.com/slavikdev/ml-runbook
• Owner: slavikdev
• Created: 2020-08-19T12:10:38.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-20T14:51:00.000Z (almost 5 years ago)
• Last Synced: 2025-01-29T11:33:31.466Z (4 months ago)
• Topics: ai, cheatsheet, machine-learning, ml, runbook
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ML Runbook

Collection of solutions for common ML problems. Contributions are welcome :)

## Dataset

### When to use a large dataset

- If you have high variance (overfitting).

- If the features are good enough for prediciton and a human expert can do manual estimation based on them.

- If the algorithm has many parameters and can represent fairly complex functions.

## High variance (overfitting)

Your model is performing very well on the training set, but poorly on the test set.

### In general

- Try getting more training examples.

- Try smaller set of features.

- When using regularization, try increasing the `lambda` parameter.

### SVM

- Try decreasing the parameter `C` (1/lambda).

- Try increasing the parameter `sigma^2`.

## High bias (underfitting)

Your model performs poorly on both training and test sets.

### In general

- Try adding more features features

- Try adding polynomial features i.e. `x^2`, `x1*x2` etc.

- When using regularization, try decreasing the `lambda` parameter.

### SVM

- Try increasing the parameter `C` (1/lambda).

- Try decreasing the parameter `sigma^2`.

## Choosing the right algorithm

### Logistic regression vs SVM

- If the number of features is large (relative to the number of examples), use either logistic regression or SVM without a kernel.

- If the number of features is small and the number of examples is intermediate (up to 10K), use SVM with Gaussian kernel.

- If the number of features is small, but the number of examples is large (over 10k), create/add more features, then use logistic regression or SVM without a kernel.

### Anomaly detection vs supervised learning

When to use anomaly detection algorithm (e.g. Gaussian distribution):

- You expect a very small number of anomalies (up to 20) and a large number of non-anomalous examples.

- You expect different types of anomalies and future anomalies may look like nothing you’ve seen so far.

When to use supervised learning:

- You expect a relatively large number of anomalies.

- Future examples are likely to be similar to the ones in the training set.