from the dirst lessons of my machine learning course, here are some key concepts about decision trees and classification methods.
between all the interesting subjects in this master, for now, ml seems to be my favorite.
decision trees: the fundamentals
decision trees work by recursively splitting data based on attribute values. the key question is: which attribute should we test first?
this is where information gain ($IG$) comes in. it measures how much a split reduces uncertainty about the class label.
finishing my thesis feels surreal after weeks researching articles and case studies. but more than that, it was about digging into the soul of a new kind of economy.
my research explored the digitalization of italian social enterprises, framed by the beautiful principles of civil economy.
civil economy emphasizes reciprocity, trust, and the common good, values that seem at odds with the fast-paced, efficiency-driven world of digital tech. so i asked myself: “how can social enterprises embrace digital tools without losing their essence?”