Research Class: Predicting peach fruit ripeness using explainable machine learning

Datum održavanja: 5. studenog 2020. u 10:30 sati online putem Jitsi platforme
Predavač: Dejan Ljubobratović, Sveučilište u Rijeci, Odjel za informatiku
Naziv predavanja: Predicting peach fruit ripeness using explainable machine learning

Abstract:

Predicting fruit ripeness allows us to choose the optimal time to harvest. The parameter by which peach ripeness is commonly represented is its firmness. As traditional methods for determining firmness of peaches are destructive, this paper uses an alternative method for determining peach ripeness which is based on peach impedance, as recommended by the domain expert.

The data set on which the data analysis is performed contains measurements obtained from a couple of hundred fruit measurements, which also include peach impedance. In our data analysis, we use one of the high accuracy machine learning models, which are called black box models and which are characterized by low interpretability. The paper presents the results of applying a black box type machine learning method, as well as methods for interpreting black box models which facilitate understanding of the model behavior for domain experts, i.e. Variable importance, Tree Surrogate, Local Interpretable Model-Agnostic Explanations and Break Down.

 

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