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). SUBSCRIBE to our channel:    / orangedatamining   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 __ 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)