========== Benchmarks ========== Scikits.learn benchmark ----------------------- This is from a benchmark developed by the `scikits.learn team `__. I ran it on my Intel Core2 6600, 2.40GHz CPU. .. table:: Results in scikits.learn ml-benchmarks ============ ======= ====== ======= ======== ============= ======== Benchmark PyMVPA Shogun Pybrain MLPy scikits.learn milk ============ ======= ====== ======= ======== ============= ======== knn **1.0** 2.23 -- 2.23 3.05 2.20 elasticnet -- -- -- 174.43 **1.0** -- lassolars -- -- -- 61.67 **1.0** -- pca -- -- -- -- **1.0** 11.11 kmeans -- 2.02 7057.02 1.61 6.74 **1.0** svm 3.35 1.20 -- -- 1.24 **1.0** ============ ======= ====== ======= ======== ============= ======== All of the results are normalised by the fastest system for each entry (which is therefore, by definition, 1.0). So, except for PCA, milk *is pretty fast* and for kmeans and SVM learning it is the fastest system. Limitations of This Benchmark ----------------------------- 1. It is very small dataset, so you do not get a feeling of how it scales. 2. It is only one dataset. 3. Since the benchmark came out, I made some changes to milk to make it go faster. I hope that other systems do the same, though, so we can have good progress.