Summary of Limits to Classification Performance by Relating Kullback-leibler Divergence to Cohen’s Kappa, By L. Crow and S. J. Watts
Limits to classification performance by relating Kullback-Leibler divergence to Cohen’s Kappa
by L. Crow, S. J. Watts
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning classification algorithms are typically evaluated by estimating metrics from the confusion matrix, using training data and cross-validation. However, these methods do not guarantee that the best possible performance has been achieved. To address this limitation, researchers have used information distance measures to estimate fundamental limits to error rates. The abstract discusses how the confusion matrix can be formulated to comply with the Chernoff-Stein Lemma, which links error rates to Kullback-Leibler divergences between probability density functions describing two classes. This leads to a key result that relates Cohen’s Kappa to the Resistor Average Distance, which is estimated from training data using kNN estimates of Kullback-Leibler divergences. The abstract also discusses the application of these methods to Monte Carlo data and real-world datasets, including Breast Cancer, Coronary Heart Disease, Bankruptcy, and Particle Identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms are used to classify things like breast cancer or heart disease. But how good are they really? Some people just look at a table that shows how well the algorithm did, but this doesn’t tell us if it could have done better. In fact, some data might be very tricky for an algorithm to work with. This paper talks about ways to figure out how well an algorithm can do based on the quality of the data and the variables used. It uses special math formulas to get a better idea of what’s possible. The results show that sometimes algorithms are not as good as we think they are, but that’s okay because it helps us understand what we need to improve. |
Keywords
* Artificial intelligence * Classification * Confusion matrix * Machine learning * Probability
