![]() Reduce the number of variables by merging correlated variables.Try to train the models on the original number of features, which take days or weeks if the number of features is too high.You have different options to deal with a huge number of features in a dataset. ![]() Large number of features in the dataset is one of the factors that affect both the training time as well as accuracy of machine learning models. However, there are still various factors that cause performance bottlenecks while developing such models. With the availability of high performance CPUs and GPUs, it is pretty much possible to solve every regression, classification, clustering and other related problems using machine learning and deep learning models.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |