In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. Rarely do we think about causation and the actual effect of a single feature variable or covariate on the target or response. Some even go so far as to say that „correlation trumps causation“ like in the book „Big Data: A Revolution That Will Transform How We Live, Work, and Think“ by Viktor Mayer-Schönberger and Kenneth Cukier. Following their reasoning, with Big Data there is no need to think about causation anymore, since nonparametric models will do just fine using correlation alone. For many practical use cases, this point of view may seem acceptable — but surely not for all.
Data Science
Hive UDFs and UDAFs with Python
Sometimes the analytical power of built-in Hive functions is just not enough. In this case it is possible to write hand-tailored User-Defined Functions (UDFs) for transformations and even aggregations which are therefore called User-Defined Aggregation Functions (UDAFs). In this post we focus on how to write sophisticated UDFs and UDAFs in Python. Weiterlesen
Death of an ELK?
This article will guide you through updating the ELK stack from version 1.x to 2.x, taking into account the correct order of its components Elasticsearch, Logstash and Kibana. Weiterlesen