Classification Learner App |

Know how to find a suitable classification model for your dataset and get the best accuracy using the Classification Learner App. Read the blog at https://mlhp.link/BlogClassificationApp

We used the J, M, and V letter datasets for a #classification task in the previous blog at https://mlhp.link/BlogClassificationML/ We performed the classification by directly coding. Now let us see how we can do it in the Classification Learner app. In coding, we used only the #KNN method, and we did not try to improve accuracy. But in the app, we can use Classification Learner to train models of these classifiers: decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, naive Bayes, and ensemble classification. In addition to training models, we can explore the data, select features, specify validation schemes, and evaluate results. We can export a model to the workspace to use the model with new data or generate MATLAB code to learn about programmatic classification. Watch this video or read blog to understand better.

0:00 Introduction
1:02 Importing the excel file
2:06 Using classification learner app
5:28 Training model
7:35 Exporting the model to the workspace
8:35 MATLAB code to check the accuracy of the model
10:29 Running the code and viewing the results

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