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This document provides an overview of lessons on evaluating machine learning classifiers using Weka. It discusses using holdout validation by splitting data into training and test sets. It also introduces k-fold cross-validation as a way to estimate classifier performance while making more efficient use of the available data. The lessons use Weka to analyze sample datasets and classify examples using different algorithms like decision trees, Naive Bayes, and rule-based classifiers. The goal is to understand

Author

Quân Phạm

Language

EN