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This Python code utilizes the decision tree algorithm from the scikit-learn library to perform banknote authentication. The code aims to analyze the impact of different train-test split ratios and training set sizes on the accuracy and size of the learned decision tree.
This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features (variance, skew, curtosis, and entropy) and a class attribute indicating whether a banknote is real or forged.