This document discusses using Matlab for machine learning tasks like classification. It covers loading and preparing a dataset, using various classification algorithms like decision trees and KNN, evaluating model performance with metrics like confusion matrices and ROC curves. It also discusses using the neural network toolbox to create and test a neural network classifier, training it on a labeled dataset and then using the trained model to classify new unlabeled data.
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Testing the classification algorithm
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The classification
algorithm must
receive the dataset
labeled to train.
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Testing the classification algorithm
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Testing the classification algorithm
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Dataset different
from the training
data, this dataset
has no labels.
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Testing the classification algorithm
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Testing the classification algorithm
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