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analyze the performance of several machine learning algorithms on the MNIST digit database.

[login to view URL] three different classifiers. First two classifiers should be decision tree and logistic regression. You can choose the third one yourself. Use a classifier that we did not cover in the lectures.

[login to view URL] (in max one page) how the third classifier works, in your own sentences.

[login to view URL] MNIST dataset into three sets: training, validation and test [login to view URL] validation set to choose best parameters of classifiers (called tuning hyperparameters)

[login to view URL] each classifier, present the classification results using 10-fold cross-validation, i.e., provide mean and standard deviation of classification accuraciesin a table.

[login to view URL] the best performing classification algorithm based on the validation set results andcompute confusion matrix on the test set. Provide 1) confusion matrixand 2) final test set accuracy on the test set.

[login to view URL] the confusion matrix and explain which digit classes are confused most often? Plot some examples of mostly confused digitpairs. Try to explain why these digits are misclassified?

[login to view URL] the accuracyof your best performing algorithm with the one you find from the [login to view URL] algorithm has the best accuracy? Assume that algorithm A is better than algorithm B. Why do you think A is better than Bin terms of classification accuracy?

Compétences : Machine Learning (ML), Algorithme, Python, Exploitation de Données

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Concernant l'employeur :
( 0 commentaires ) Istanbul, Turkey

Nº du projet : #30376925

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