Feature Engineering in kdb+

28 Jun 2018 | , , ,

Feature engineering is an essential part of the machine learning pipeline. In this blog, Fionnuala Carr discusses the feature engineering JupyterQ notebook, which includes an investigation of four different scaling , their impact on the k-Nearest Neighbors classifiers and the impact of using one-hot encoding.

Kx on the Google Cloud Platform

26 Jun 2018 | , , , ,

At Kx25, the international kdb+ user conference held in New York City on May 18th, Kx announced that kdb+ is now available on the Google Cloud Launcher. Antonio Zurlo of Google Cloud Platform (GCP) gave a presentation about Google Cloud and described an example of how to use kdb+ on the GCP.

Dimensionality Reduction in kdb+

14 Jun 2018 | , , , , ,

Dimensionality reduction methods have been the focus of much interest within the statistics and machine learning communities for a range of applications. These techniques have a long history as being methods for data pre-processing. Dimensionality reduction is the mapping of data to a lower dimensional space such that uninformative variance in the data is discarded. By doing this we hope to retain only that data that is meaningful to our machine learning problem. In addition, by finding a lower-dimensional representation of a dataset we hope we can improve the efficiency and accuracy of machine learning models.