Overfitting with Classification Trees
In this video, we examine how classification trees work with the Iris dataset, assess model accuracy using the Predictions widget, and plot the results in the Distributions widget. The tree performs well, with a 98% accuracy rate. We also experiment with randomized data and observe that the tree still makes mostly accurate predictions.
This video is a part of Introduction to Data Science video series that dives into machine learning, visual analytics, and joys of interactive data analysis using Orange Data Mining software (https://orangedatamining.com).
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The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation.
#machinelearning #orange #visualanalytics #datamining
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Written by: Blaž Zupan (http://biolab.si/blaz)
Presented by: Noah Novšak
Production and edit: Lara Zupan
Intro/outro: Agnieszka Rovšnik
Music by: Damjan Jović – Dravlje Rec
Orange is developed by Biolab at University of Ljubljana (https://www.biolab.si)
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