Orange: Misclassifications - Revision history

Onnowpurbo: /* Workflow Breakdown: */

2025-02-11T09:09:27Z

Workflow Breakdown:

Onnowpurbo at 09:09, 11 February 2025

2025-02-11T09:09:10Z

Onnowpurbo at 02:23, 28 February 2020

2020-02-28T02:23:15Z

Onnowpurbo at 02:21, 28 February 2020

2020-02-28T02:21:37Z

Onnowpurbo at 20:26, 8 January 2020

2020-01-08T20:26:00Z

Onnowpurbo at 23:33, 7 January 2020

2020-01-07T23:33:30Z

Onnowpurbo at 23:32, 7 January 2020

2020-01-07T23:32:25Z

Onnowpurbo at 12:03, 7 January 2020

2020-01-07T12:03:19Z

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

@@ Line 29: / Line 29: @@
 * 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.
 * It '''evaluates performance''' using a confusion matrix.
@@ Line 36: / Line 36: @@
 This workflow helps in '''understanding classification errors''' and '''improving model performance''' by selecting better feature projections or classification methods.
 ==Source==

← 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.

@@ Line 1: / Line 1: @@
 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]]

@@ Line 1: / Line 1: @@
 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]]

@@ Line 1: / Line 1: @@
 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]]

@@ Line 1: / Line 1: @@
 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.
@@ Line 7: / Line 9: @@
 * https://service.biolab.si/download/workflow?name=470-misclassification-scatterplot.ows&domain=orange
 ==Pranala Menarik==
 * [[Orange]]

← 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]]

← 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]]