DATA
transformation
An initiative by
permanent beta


AI & Analytics
Algorithm selection
Aspiring Data Scientists with a wish to deepdive on the most important technologies.
Aimed at:
Delivery method:
Interactive classroom training combining theory with practical exercises
Prerequisites:
Required preparation:
(Applied) knowledge of at least 1 data science technique
Bring a laptop with RStudio installed
2 hours
Foundation
Duration:
Skill level:
Prefered group size:
+/-10 participants per trainer
Course description
Data Scientists usually boast about one particular algorithm to be used on each and every occasion. But aren't various algorithms more or less suitable to different problems? While many of focus on building trees or (logistic) regression solutions, other popular methods are outlined in this session (SVM and Nearest Neighbour), as well as stacking those solutions. Nevertheless, there is dispute on whether the search for the 'best algorithm fit' for a specific problem is worth the effort. We'll discover this area using a piece of example code in R.
Learning objective
Upon completion of this training, participants will be able to distinguish pros and cons of various algorithmic setups, and be able to use those methods, even in mixed form, for realistic cases.