Orange: Confusion Matrix - Revision history

Onnowpurbo: /* Contoh */

2020-02-27T23:23:33Z

Contoh

← Older revision		Revision as of 23:23, 27 February 2020
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	Test & Score memperoleh data dari File dan dua learning algorithm dari Naive Bayes dan Tree. Test & Score melakukan cross-validation atau prosedur train-and-test lainnya untuk memperoleh class prediction oleh ke dua algorima untuk semua (atau sebagian) data instance. Hasil test dikirimkan ke Confusion Matrix, dimana kita dapat mengamati berapa instance yang misclassified dan kenapa.		Test & Score memperoleh data dari File dan dua learning algorithm dari Naive Bayes dan Tree. Test & Score melakukan cross-validation atau prosedur train-and-test lainnya untuk memperoleh class prediction oleh ke dua algorima untuk semua (atau sebagian) data instance. Hasil test dikirimkan ke Confusion Matrix, dimana kita dapat mengamati berapa instance yang misclassified dan kenapa.

	~~In the~~ output, ~~we used~~ Data ~~Table to show the instances we selected in the~~ confusion matrix. ~~If we~~, ~~for instance~~, ~~click~~ Misclassified, ~~the table will contain all instances which were misclassified by the selected method~~.		Dalam output, kita menggunakan Tabel Data untuk menunjukkan contoh yang kita pilih dalam confusion matrix. Jika kita, misalnya, klik Misclassified, tabel akan berisi semua instance yang salah diklasifikasikan oleh metode yang dipilih.

	~~The~~ Scatter Plot ~~gets two sets of~~ data. ~~From the~~ File ~~widget it gets the complete~~ data, ~~while the~~ confusion matrix ~~sends only the selected~~ data, ~~misclassifications for instance~~. ~~The scatter plot will show all the~~ data, ~~with~~ bold ~~symbols representing the selected~~ data.		Scatter Plot mendapat dua set data. Dari widget File, ia mendapatkan data lengkap, sementara confusion matrix hanya mengirim data yang dipilih, misalnya data misklasifikasi. Scatter Plot akan menampilkan semua data, dengan simbol bold yang mewakili data yang dipilih.

	[[File:ConfusionMatrix-Example.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-Example.png\|center\|200px\|thumb]]

Onnowpurbo: /* Contoh */

2020-02-27T21:54:48Z

Contoh

← Older revision		Revision as of 21:54, 27 February 2020
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	[[File:ConfusionMatrix-Example.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-Example.png\|center\|200px\|thumb]]




	==Referensi==		==Referensi==

Onnowpurbo at 21:40, 27 February 2020

2020-02-27T21:40:48Z

← Older revision		Revision as of 21:40, 27 February 2020
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	~~Shows proportions between the~~ predicted ~~and~~ actual class.		Memperlihatkan proporsi antara predicted dan actual class.

	==Input==		==Input==
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	Data: data with the additional information on whether a data instance was selected		Data: data with the additional information on whether a data instance was selected

	~~The~~ Confusion Matrix ~~gives the number~~/~~proportion of instances between the~~ predicted ~~and~~ actual class. ~~The selection of the elements in the matrix feeds the corresponding instances into the~~ output ~~signal~~. ~~This way~~, ~~one can observe which specific instances were misclassified and how~~.		Confusion Matrix memberikan jumlah / proporsi instance antara predicted dan actual class. Pemilihan elemen-elemen dalam matriks memberikan instance yang sesuai ke dalam output. Dengan cara ini, kita dapat mengamati instance spesifik mana yang salah klasifikasi dan bagaimana.

	~~The widget usually gets the evaluation results from~~ Test & Score; ~~an example of the schema is shown below~~.		Widget biasanya mendapatkan hasil evaluasi dari Test & Score; contoh skema ditunjukkan di bawah ini.

	[[File:ConfusionMatrix-stamped.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-stamped.png\|center\|200px\|thumb]]
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	==Contoh==		==Contoh==

	~~The following workflow demonstrates what this widget can be used for.~~		Workflow berikut mendemonstrasikan penggunakan Widget Confusion Matrix

	[[File:ConfusionMatrix-Schema.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-Schema.png\|center\|200px\|thumb]]

	Test & Score ~~gets the~~ data ~~from~~ File ~~and two~~ learning ~~algorithms from~~ Naive Bayes ~~and~~ Tree. ~~It performs~~ cross-validation ~~or some other~~ train-and-test ~~procedures to get~~ class ~~predictions by both algorithms for all~~ (~~or some~~) data ~~instances~~. ~~The~~ test ~~results are fed into the~~ Confusion Matrix, ~~where we can observe how many instances were~~ misclassified ~~and in which way~~.		Test & Score memperoleh data dari File dan dua learning algorithm dari Naive Bayes dan Tree. Test & Score melakukan cross-validation atau prosedur train-and-test lainnya untuk memperoleh class prediction oleh ke dua algorima untuk semua (atau sebagian) data instance. Hasil test dikirimkan ke Confusion Matrix, dimana kita dapat mengamati berapa instance yang misclassified dan kenapa.

	In the output, we used Data Table to show the instances we selected in the confusion matrix. If we, for instance, click Misclassified, the table will contain all instances which were misclassified by the selected method.		In the output, we used Data Table to show the instances we selected in the confusion matrix. If we, for instance, click Misclassified, the table will contain all instances which were misclassified by the selected method.

Onnowpurbo at 00:43, 29 January 2020

2020-01-29T00:43:32Z

← Older revision		Revision as of 00:43, 29 January 2020
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	Shows proportions between the predicted and actual class.		Shows proportions between the predicted and actual class.

	~~Inputs~~		==Input==

	Evaluation results: results of testing classification algorithms		Evaluation results: results of testing classification algorithms

	~~Outputs~~		==Output==

	Selected Data: data subset selected from confusion matrix		Selected Data: data subset selected from confusion matrix
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	[[File:ConfusionMatrix-stamped.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-stamped.png\|center\|200px\|thumb]]

	When evaluation results contain data on multiple learning algorithms, we have to choose one in the Learners box. The snapshot shows the confusion matrix for Tree and Naive Bayesian models trained and tested on the iris data. The right-hand side of the widget contains the matrix for the naive Bayesian model (since this model is selected on the left). Each row corresponds to a correct class, while columns represent the predicted classes. For instance, four instances of Iris-versicolor were misclassified as Iris-virginica. The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).		* When evaluation results contain data on multiple learning algorithms, we have to choose one in the Learners box. The snapshot shows the confusion matrix for Tree and Naive Bayesian models trained and tested on the iris data. The right-hand side of the widget contains the matrix for the naive Bayesian model (since this model is selected on the left). Each row corresponds to a correct class, while columns represent the predicted classes. For instance, four instances of Iris-versicolor were misclassified as Iris-virginica. The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).
	In Show, we select what data we would like to see in the matrix.		* In Show, we select what data we would like to see in the matrix.
	Number of instances shows correctly and incorrectly classified instances numerically.		** Number of instances shows correctly and incorrectly classified instances numerically.
	Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.		** Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.
	Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae.		** Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae.

	[[File:ConfusionMatrix-propTrue.png\|center\|200px\|thumb]]		[[File:ConfusionMatrix-propTrue.png\|center\|200px\|thumb]]

	In Select, you can choose the desired output.		* In Select, you can choose the desired output.
	Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.		** Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.
	Misclassified selects the misclassified instances.		** Misclassified selects the misclassified instances.
	None annuls the selection. As mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).		** None annuls the selection. As mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).
	When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options Predictions and/or Probabilities are checked.
	The widget outputs every change if Send Automatically is ticked. If not, the user will need to click Send Selected to commit the changes.		* When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options Predictions and/or Probabilities are checked.
	Produce a report.		* The widget outputs every change if Send Automatically is ticked. If not, the user will need to click Send Selected to commit the changes.
			* Produce a report.

	==Contoh==		==Contoh==

Onnowpurbo at 04:54, 23 January 2020

2020-01-23T04:54:36Z

← Older revision		Revision as of 04:54, 23 January 2020
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	Number of instances shows correctly and incorrectly classified instances numerically.		Number of instances shows correctly and incorrectly classified instances numerically.
	Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.		Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.
	Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae. ~~../../_images/~~ConfusionMatrix-propTrue.png		Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae.

			[[File:ConfusionMatrix-propTrue.png\|center\|200px\|thumb]]

	In Select, you can choose the desired output.		In Select, you can choose the desired output.
	Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.		Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.

Onnowpurbo at 04:53, 23 January 2020

2020-01-23T04:53:01Z

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/confusionmatrix.html Shows proportions between the predicted and actual class. Inputs Evaluation..."

2020-01-16T07:25:28Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/confusionmatrix.html Shows proportions between the predicted and actual class. Inputs Evaluation..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/confusionmatrix.html

Shows proportions between the predicted and actual class.

Inputs

Evaluation results: results of testing classification algorithms

Outputs

Selected Data: data subset selected from confusion matrix

Data: data with the additional information on whether a data instance was selected

The Confusion Matrix gives the number/proportion of instances between the predicted and actual class. The selection of the elements in the matrix feeds the corresponding instances into the output signal. This way, one can observe which specific instances were misclassified and how.

The widget usually gets the evaluation results from Test & Score; an example of the schema is shown below.

../../_images/ConfusionMatrix-stamped.png

When evaluation results contain data on multiple learning algorithms, we have to choose one in the Learners box. The snapshot shows the confusion matrix for Tree and Naive Bayesian models trained and tested on the iris data. The right-hand side of the widget contains the matrix for the naive Bayesian model (since this model is selected on the left). Each row corresponds to a correct class, while columns represent the predicted classes. For instance, four instances of Iris-versicolor were misclassified as Iris-virginica. The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).

In Show, we select what data we would like to see in the matrix.

Number of instances shows correctly and incorrectly classified instances numerically.

Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.

Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae. ../../_images/ConfusionMatrix-propTrue.png

In Select, you can choose the desired output.

Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.

Misclassified selects the misclassified instances.

None annuls the selection. As mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).

When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options Predictions and/or Probabilities are checked.

The widget outputs every change if Send Automatically is ticked. If not, the user will need to click Send Selected to commit the changes.

Produce a report.

Example

The following workflow demonstrates what this widget can be used for.

../../_images/ConfusionMatrix-Schema.png

Test & Score gets the data from File and two learning algorithms from Naive Bayes and Tree. It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the Confusion Matrix, where we can observe how many instances were misclassified and in which way.

In the output, we used Data Table to show the instances we selected in the confusion matrix. If we, for instance, click Misclassified, the table will contain all instances which were misclassified by the selected method.

The Scatter Plot gets two sets of data. From the File widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.

../../_images/ConfusionMatrix-Example.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/evaluate/confusionmatrix.html

==Orange==

* [[Orange]]

← Older revision		Revision as of 04:53, 23 January 2020
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	Selected Data: data subset selected from confusion matrix		Selected Data: data subset selected from confusion matrix

	Data: data with the additional information on whether a data instance was selected		Data: data with the additional information on whether a data instance was selected

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	The widget usually gets the evaluation results from Test & Score; an example of the schema is shown below.		The widget usually gets the evaluation results from Test & Score; an example of the schema is shown below.

	~~../../_images/~~ConfusionMatrix-stamped.png		[[File:ConfusionMatrix-stamped.png\|center\|200px\|thumb]]

	When evaluation results contain data on multiple learning algorithms, we have to choose one in the Learners box. The snapshot shows the confusion matrix for Tree and Naive Bayesian models trained and tested on the iris data. The right-hand side of the widget contains the matrix for the naive Bayesian model (since this model is selected on the left). Each row corresponds to a correct class, while columns represent the predicted classes. For instance, four instances of Iris-versicolor were misclassified as Iris-virginica. The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).		When evaluation results contain data on multiple learning algorithms, we have to choose one in the Learners box. The snapshot shows the confusion matrix for Tree and Naive Bayesian models trained and tested on the iris data. The right-hand side of the widget contains the matrix for the naive Bayesian model (since this model is selected on the left). Each row corresponds to a correct class, while columns represent the predicted classes. For instance, four instances of Iris-versicolor were misclassified as Iris-virginica. The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).

	In Show, we select what data we would like to see in the matrix.		In Show, we select what data we would like to see in the matrix.

	Number of instances shows correctly and incorrectly classified instances numerically.		Number of instances shows correctly and incorrectly classified instances numerically.

	Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.		Proportions of predicted shows how many instances classified as, say, Iris-versicolor are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.

	Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae. ../../_images/ConfusionMatrix-propTrue.png		Proportions of actual shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae. ../../_images/ConfusionMatrix-propTrue.png

	In Select, you can choose the desired output.		In Select, you can choose the desired output.

	Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.		Correct sends all correctly classified instances to the output by selecting the diagonal of the matrix.

	Misclassified selects the misclassified instances.		Misclassified selects the misclassified instances.

	None annuls the selection. As mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).		None annuls the selection. As mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).

	When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options Predictions and/or Probabilities are checked.		When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options Predictions and/or Probabilities are checked.

	The widget outputs every change if Send Automatically is ticked. If not, the user will need to click Send Selected to commit the changes.		The widget outputs every change if Send Automatically is ticked. If not, the user will need to click Send Selected to commit the changes.

	Produce a report.		Produce a report.

	~~Example~~		==Contoh==

	The following workflow demonstrates what this widget can be used for.		The following workflow demonstrates what this widget can be used for.

	~~../../_images/~~ConfusionMatrix-Schema.png		[[File:ConfusionMatrix-Schema.png\|center\|200px\|thumb]]

	Test & Score gets the data from File and two learning algorithms from Naive Bayes and Tree. It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the Confusion Matrix, where we can observe how many instances were misclassified and in which way.		Test & Score gets the data from File and two learning algorithms from Naive Bayes and Tree. It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the Confusion Matrix, where we can observe how many instances were misclassified and in which way.
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	The Scatter Plot gets two sets of data. From the File widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.		The Scatter Plot gets two sets of data. From the File widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.

	~~../../_images/~~ConfusionMatrix-Example.png		[[File:ConfusionMatrix-Example.png\|center\|200px\|thumb]]