Difference between revisions of "Orange: Misclassifications"

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Sumber: https://orange.biolab.si/workflows/
 
Sumber: https://orange.biolab.si/workflows/
  
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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.
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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]]
  
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The image represents an '''Orange Data Mining workflow''' designed for evaluating a '''classification model''' using logistic regression and visualizing misclassifications.
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=='''Workflow Breakdown:'''==
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1. '''File (Data Input)'''
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* This node loads the dataset (likely the '''Iris dataset''') as input for further processing.
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2. '''Learner (Logistic Regression)'''
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* The '''logistic regression''' model is used as the classification algorithm.
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* The note in green suggests that this model can be replaced with any other classification method.
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3. '''Test & Score'''
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* This node is responsible for evaluating the '''logistic regression model'''.
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* It measures the model’s performance using various metrics like accuracy, precision, recall, and F1-score.
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4. '''Confusion Matrix'''
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* Displays the types of '''misclassifications''' in the dataset.
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* The note in red highlights that for the '''Iris dataset''', '''Iris virginica''' and '''Iris versicolor''' are often confused with each other.
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5. '''Scatter Plot'''
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* Visualizes the data distribution and classification results.
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* The note suggests that '''misclassifications''' are best seen in '''petal length vs. petal width projection'''.
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=='''Purpose of the Workflow:'''==
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* The workflow '''trains a logistic regression classifier''' on the Iris dataset.
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* It '''evaluates performance''' using a confusion matrix.
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* '''Misclassifications are visualized''' using a scatter plot.
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* Users can '''swap the classification method''' to compare different algorithms.
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This workflow helps in '''understanding classification errors''' and '''improving model performance''' by selecting better feature projections or classification methods.
  
 
==Source==
 
==Source==

Latest revision as of 16:09, 11 February 2025

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 berada dekat / wilayah daerah yang class-nya berbatasan terlihat di scatter plot projection.

Misclassifications.png


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.

Source

Referensi

Pranala Menarik

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