Orange: Misclassifications: Difference between revisions

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Cross-validation of, say, logistic regression can expose the data instances which were misclassified. There are six such instances for iris dataset and ridge-regularized logistic regression. We can select different types of misclassification in Confusion Matrix and highlight them in the Scatter Plot. No surprise: the misclassified instances are close to the class-bordering regions in the scatter plot projection.
Cross-validation dari, misalnya, logistic regression dapat mengekspos instance data yang salah klasifikasi. Ada enam contoh untuk dataset iris dan ridge-regularized logistic regression. Kita dapat memilih berbagai jenis kesalahan klasifikasi dalam Confusion Matrix dan menyorotnya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi dekat dengan daerah yang berbatasan dengan kelas dalam scatter plot projection.


[[File:Misclassifications.png|center|200px|thumb]]
[[File:Misclassifications.png|center|200px|thumb]]

Revision as of 23:32, 7 January 2020

Cross-validation dari, misalnya, logistic regression dapat mengekspos instance data yang salah klasifikasi. Ada enam contoh untuk dataset iris dan ridge-regularized logistic regression. Kita dapat memilih berbagai jenis kesalahan klasifikasi dalam Confusion Matrix dan menyorotnya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi dekat dengan daerah yang berbatasan dengan kelas dalam scatter plot projection.


Source

Pranala Menarik

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