A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Hi
Could you please help me to understand if MIT covers datasets mentioned here
Training datasets of sizes 10K, 100K, 1M, 10M are generated
from the well-known airline dataset (using years 2005 and 2006). A test set of size 100K is generated from the same (using year 2007).
Hi Could you please help me to understand if MIT covers datasets mentioned here Training datasets of sizes 10K, 100K, 1M, 10M are generated from the well-known airline dataset (using years 2005 and 2006). A test set of size 100K is generated from the same (using year 2007).