How to find F1 Score, accuracy, cross entropy, precision, recall using different classifiers

I was given a train dataset and a test dataset. The train dataset contains 12000 rows (data) and 53 attributes (columns) and one target column.The test dataset contains 2000 rows(data) and 53 attributes (columns) and one target column (needs to be predicted). I need to identify the best classifier to predict the test dataset targets based on accuracy, F1, cross entropy, recall, precision.

[the cyclist]

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Answer 1

[Nihal Reddy]

I understand you want to compare different classifiers based on metrics like accuracy, F1, cross entropy, recall, precision on your test dataset.

You can refer to the following MATLAB documentation for understanding Supervised and semi-supervised classification algorithms for binary and multiclass problems-

https://www.mathworks.com/help/stats/classification.html

For comparing different classifers you can use the Classification Learner App to interactively train, validate, and tune classification models. Please refer to the following MATLAB documentation for Classification Learner App resources-

https://www.mathworks.com/help/stats/classification-learner-app.html