|||

Parameter Optimization in Rapidminer 5.0

In several of my video tutorials I assign different parameters for my learning model on the fly.” Of course the question any astute reader/viewer should ask is, why did you choose those parameters instead of another combination?”

That’s a great question and the answer is, well I just choose those parameters to illustrate my point for the video.” While this answer is not at all satisfying to the astute reader/viewer, it does lead us to ask the most important question of all, what are the right parameters to choose?”

This can be  answered very well if you were to use Rapidminer’s Parameter Optimization operator in your initial data discovery phase. This operator allows you to choose some or all of the parameters in your experiment and iterate different values for them to meet some specific requirement on your part (i.e. performance).

For example, if you were using the Neural Net operator and didn’t know what to set your learning and momentum parameters to, to get the best classification accuracy, you would use the Parameter Optimization operator to interate different combinations of those parameters to find the best accuracy.

Once the Parameter  Optimization  operator determines those values, you can input  them into your experiment and truly optimize your model for performance! See below for an actual output from a parameter  optimization  model I’m working on. You can see that Rapidminer indicated that momentum of 0.3 and a learning rate of 0.6 was the best parameter settings to maximize the accuracy rate and minimize the classification error.

While is operator is a fantastic feature (they got evolutionary optimizers too!) for us data modelers, its a massive computer resource suck.  I would advise anyone using this operator to have a very powerful server or computer, with oodles of memory, to run your iterations.

Up next Rapidminer Web Mining Extension Now Available! Just wanted to pass this along, in case you haven’t seen it already but Rapidminer has released the Web Mining extension for version 5.0. All you RCOMM 2010 - First Rapidminer Conference! Hi all! I’m almost back!  I’m easing my way back into posting again as work somewhat quiets down.  Thank you so much for your emails and
Latest posts Revisiting GOOG, GE, NE, IYR from 2007 The Ye Old Blog List Motorola: Then and Now EWM Redux Testing for mean reversion with Python & developing simple VIX system - Talaikis unsorted - Tadas Talaikis Blog Steps to calculate centroids in cluster using K-means clustering algorithm - Data Science Central Basics of Statistical Mean Reversion Testing - QuantStart Algorithmic trading in less than 100 lines of Python code - O’Reilly Media Interpreting Machine Learning Models Microsoft the AI Powerhouse Investing in the S&P500 still beats AI Trading Microsoft makes a push to simplify machine learning | TechCrunch 10 Great Articles On Python Development — Hacker Noon Introduction to Keras Democratising Machine learning with H2O — Towards Data Science Getting started with Python datatable | Kaggle Phone Addiction Machine Learning Making Pesto Tastier 5 Dangerous Things You Should Let Your Kids Do The Pyschology of Writing Investing in 2019 and beyond TensorFlow and High Level APIs Driving Marketing Performance with H2O Driverless AI Machine Learning and Data Munging in H2O Driverless AI with datatable Making AI Happen Without Getting Fired Latest Musings from a Traveling Sales Engineer The Night before H2O World 2019 Why Forex Trading is Frustrating Functional Programming in Python Automatic Feature Engineering with Driverless AI Ray Dalio's Pure Alpha Fund