Here we will present the setup for single machine to run time consuming machine learning experiments like feature selection using different machine learning models. First we will create python program which runs single experiment. We will use argparse library to…

Feature selection in low signal-to-noise environments like finance. In the following we will create a feature selection function which would work on XGBoost models as well as Tensorflow and simple sklearn models. We will use univariate as well as other…

In many time series machine learning problems the with large number of features the raw data might contain – abnormal / extreme points – discontinuities – stale data To help with determining quickly abnormal or extreme points we can use…

Sometimes it is preferable to use simple machine learning algorithms such as logistic regression due to speed and explainability. But usually these simple algorithms do not incorporate interactions of the features (in contrary to , say, neural networks, where sum/difference…

For machine learning algorithms to work well, it’s usually useful to remove noise from features.For time-series this can be achieved in several ways, such as moving averages, applying sign transform, or applying low pass filter. Other, more simple way is…