Orange: Misclassifications - Revision history

Unknown user: /* Workflow Breakdown: */

2025-02-11T09:09:27Z

Workflow Breakdown:

← Older revision		Revision as of 09:09, 11 February 2025
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	* The note suggests that '''misclassifications''' are best seen in '''petal length vs. petal width projection'''.		* The note suggests that '''misclassifications''' are best seen in '''petal length vs. petal width projection'''.

	~~###~~ '''Purpose of the Workflow:'''		=='''Purpose of the Workflow:'''==
	* The workflow '''trains a logistic regression classifier''' on the Iris dataset.		* The workflow '''trains a logistic regression classifier''' on the Iris dataset.
	* It '''evaluates performance''' using a confusion matrix.		* It '''evaluates performance''' using a confusion matrix.
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	This workflow helps in '''understanding classification errors''' and '''improving model performance''' by selecting better feature projections or classification methods.		This workflow helps in '''understanding classification errors''' and '''improving model performance''' by selecting better feature projections or classification methods.


	==Source==		==Source==

Unknown user at 09:09, 11 February 2025

2025-02-11T09:09:10Z

← Older revision		Revision as of 09:09, 11 February 2025
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	[[File:Misclassifications.png\|center\|200px\|thumb]]		[[File:Misclassifications.png\|center\|200px\|thumb]]



			The image represents an '''Orange Data Mining workflow''' designed for evaluating a '''classification model''' using logistic regression and visualizing misclassifications.

			=='''Workflow Breakdown:'''==
			1. '''File (Data Input)'''
			* This node loads the dataset (likely the '''Iris dataset''') as input for further processing.

			2. '''Learner (Logistic Regression)'''
			* The '''logistic regression''' model is used as the classification algorithm.
			* The note in green suggests that this model can be replaced with any other classification method.

			3. '''Test & Score'''
			* This node is responsible for evaluating the '''logistic regression model'''.
			* It measures the model’s performance using various metrics like accuracy, precision, recall, and F1-score.

			4. '''Confusion Matrix'''
			* Displays the types of '''misclassifications''' in the dataset.
			* The note in red highlights that for the '''Iris dataset''', '''Iris virginica''' and '''Iris versicolor''' are often confused with each other.

			5. '''Scatter Plot'''
			* Visualizes the data distribution and classification results.
			* The note suggests that '''misclassifications''' are best seen in '''petal length vs. petal width projection'''.

			### '''Purpose of the Workflow:'''
			* The workflow '''trains a logistic regression classifier''' on the Iris dataset.
			* It '''evaluates performance''' using a confusion matrix.
			* '''Misclassifications are visualized''' using a scatter plot.
			* Users can '''swap the classification method''' to compare different algorithms.

			This workflow helps in '''understanding classification errors''' and '''improving model performance''' by selecting better feature projections or classification methods.

Onnowpurbo at 02:23, 28 February 2020

2020-02-28T02:23:15Z

← Older revision		Revision as of 02:23, 28 February 2020
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	Sumber: https://orange.biolab.si/workflows/		Sumber: https://orange.biolab.si/workflows/

	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 menayangkannya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi dekat ~~dengan~~ daerah yang berbatasan ~~dengan class dalam~~ 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 menayangkannya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi berada dekat / wilayah daerah yang class-nya berbatasan terlihat di scatter plot projection.

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

Onnowpurbo at 02:21, 28 February 2020

2020-02-28T02:21:37Z

← Older revision		Revision as of 02:21, 28 February 2020
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	Sumber: https://orange.biolab.si/workflows/		Sumber: https://orange.biolab.si/workflows/

	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 menayangkannya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi dekat dengan daerah yang berbatasan dengan ~~kelas~~ dalam 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 menayangkannya dalam Scatter Plot. Tidak mengherankan: contoh kesalahan klasifikasi dekat dengan daerah yang berbatasan dengan class dalam scatter plot projection.

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

Onnowpurbo at 20:26, 8 January 2020

2020-01-08T20:26:00Z

← Older revision		Revision as of 20:26, 8 January 2020
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	Sumber: https://orange.biolab.si/workflows/		Sumber: https://orange.biolab.si/workflows/

	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.		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 menayangkannya 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]]

Onnowpurbo at 23:33, 7 January 2020

2020-01-07T23:33:30Z

← Older revision		Revision as of 23:33, 7 January 2020
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			Sumber: https://orange.biolab.si/workflows/

	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.		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.

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	* https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange		* https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange

			==Referensi==

			* https://orange.biolab.si/workflows/

	==Pranala Menarik==		==Pranala Menarik==

	* [[Orange]]		* [[Orange]]

Onnowpurbo at 23:32, 7 January 2020

2020-01-07T23:32:25Z

← Older revision		Revision as of 23:32, 7 January 2020
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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]]

Onnowpurbo at 12:03, 7 January 2020

2020-01-07T12:03:19Z

← Older revision		Revision as of 12:03, 7 January 2020
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	* https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange		* https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange

			==Pranala Menarik==

			* [[Orange]]

Onnowpurbo: Created page with "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 logis..."

2020-01-07T12:03:03Z

Created page with "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 logis..."

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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.

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

==Source==

* https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange