Statistical significance of a ML model...

• fog37
In summary, determining if a ML model is statistically significant involves using tests such as t-tests or F-tests for models like linear regression or logistic regression. However, for other models like decision trees, SVM, or neural nets, there is a subfield called uncertainty quantification that is actively developing methods to determine statistical significance. It is important to set aside a part of the input data for testing purposes to avoid bias in the results.
fog37
TL;DR Summary
Determining if a ML model is statistically significant...
Hello,

How do we check if a ML model is statistically significant? For models like linear regression, logistic regression, etc. there are tests (t-tests, F-tests, etc.) that will tell us if the model, trained on some dataset, is statistically significant or not.

But in the case of ML models, like decision trees, SVM, or neural nets, how do we determine if the model is statistically significant? I have not seen any specific test to do that...

Thank you!

There is a whole subfield on this called UQ - uncertainty quantification. It is an area or active development.

fog37 said:
TL;DR Summary: Determining if a ML model is statistically significant...

But in the case of ML models, like decision trees, SVM, or neural nets, how do we determine if the model is statistically significant? I have not seen any specific test to do that...
The t test will work with any predictive model. You're supposed to set aside a part of the input data, and not use it in your model and use it for testing later. (Because predicting your input data with a ML model is cheating). For a yes/no model, you can score a 1 for correct, and 0 for wrong, and you can compare it other ways to predict the outcomes (or random guessing),

1. How do you determine the statistical significance of a machine learning model?

To determine the statistical significance of a machine learning model, you can perform hypothesis testing using techniques such as p-values, confidence intervals, and cross-validation. These methods help determine if the performance of the model is significantly better than random chance.

2. Why is it important to assess the statistical significance of a machine learning model?

Assessing the statistical significance of a machine learning model is important because it helps determine if the results obtained are reliable and not due to random chance. It provides confidence in the performance of the model and helps in making informed decisions based on the model's predictions.

3. What is the relationship between statistical significance and model performance?

The statistical significance of a machine learning model is related to its performance in terms of how well it generalizes to unseen data. A model that is statistically significant is more likely to have consistent and reliable performance across different datasets, indicating that its predictions are not just due to luck.

4. Can a machine learning model be statistically significant but still perform poorly?

Yes, a machine learning model can be statistically significant but still perform poorly in terms of predictive accuracy. Statistical significance only indicates that the results are not due to random chance, but it does not guarantee that the model is accurate or useful for making predictions.

5. How can you improve the statistical significance of a machine learning model?

To improve the statistical significance of a machine learning model, you can try techniques such as feature selection, hyperparameter tuning, and increasing the size of the training dataset. These methods can help enhance the model's performance and make its results more statistically significant.

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