Orange: Naive Bayes - Revision history

Onnowpurbo at 10:30, 5 April 2020

2020-04-05T10:30:01Z

← Older revision		Revision as of 10:30, 5 April 2020
Line 2:		Line 2:


	~~Klasifikasi~~ probabilistik ~~yang~~ cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature.		Widget Naive Bayes dapat melakukan klasifikasi probabilistik secara cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature.

	==Input==		==Input==
Line 14:		Line 14:
	Model: trained model		Model: trained model

	Widget Naive Bayes mempelajari model Naive Bayesian dari data. Widget ~~ini~~ hanya berfungsi untuk task classification.		Widget Naive Bayes mempelajari model Naive Bayesian dari data. Widget Naive Bayes hanya berfungsi untuk task classification.

	[[File:NaiveBayes-stamped.png\|center\|~~200px~~\|thumb]]		[[File:NaiveBayes-stamped.png\|center\|400px\|thumb]]

	Widget ~~ini~~ memiliki dua opsi: nama yang akan ditampilkan di widget lain dan menghasilkan report. Nama standarnya adalah Naive Bayes. Ketika kita mengubahnya, kita perlu menekan Apply.		Widget Naive Bayes memiliki dua opsi: nama yang akan ditampilkan di widget lain dan menghasilkan report. Nama standarnya adalah Naive Bayes. Ketika kita mengubahnya, kita perlu menekan Apply.

	==Contoh==		==Contoh==

	Berikut ini kita akan memperlihatkan dua penggunaan dari Widget Naive Bayes. Pertama, kita akan membandingkan hasil dari Naive Bayes dengan model lain, yaitu Random Forest. Kita meng-connect iris data dari Widget File langsung ke Widget Test & Score. Kita juga meng-connect Widget Naive Bayes dan Widget Random Forest ke Widget Test & Score dan kita amati score prediksi-nya.		Berikut ini kita akan memperlihatkan dua penggunaan dari Widget Naive Bayes.

	[[File~~:NaiveBayes~~-~~classification~~.~~png\|center\|200px\|thumb]]~~		Dalam contoh ini, kita akan membandingkan hasil dari widget Naive Bayes dengan model lain, yaitu widget Random Forest. Kita meng-connect iris data dari Widget File langsung ke Widget Test & Score. Kita juga meng-connect Widget Naive Bayes dan Widget Random Forest ke Widget Test & Score dan kita amati score prediksi-nya.

	Skema kedua menunjukkan kualitas prediksi yang dihasilkan oleh Naive Bayes. Kita menyambungkan widget Test & Score dengan Naive Bayes learner dan kemudian mengirim data yang dihasilkan ke Confusion Matrix. Kami juga menghubungkan widget Scatter Plot dengan widget File~~. Kemudian kita memilih contoh-contoh kesalahan klasifikasi di widget Confusion Matrix dan menampilkannya ke widget Scatter Plot. Titik~~-~~titik bold di widget Scatter Plot adalah contoh kesalahan klasifikasi dari Naive Bayes~~.		[[File:NaiveBayes-classification.png\|center\|600px\|thumb]]

	[[File:NaiveBayes-visualize.png\|center\|~~200px~~\|thumb]]		Contoh berikut ini, menunjukkan kualitas prediksi yang dihasilkan oleh widget Naive Bayes. Kita menyambungkan widget Test & Score dengan widget Naive Bayes learner dan kemudian mengirim data yang dihasilkan ke widget Confusion Matrix. Kita juga menghubungkan widget Scatter Plot dengan widget File. Kemudian kita memilih contoh-contoh kesalahan klasifikasi di widget Confusion Matrix dan menampilkannya ke widget Scatter Plot. Titik-titik bold di widget Scatter Plot adalah contoh kesalahan klasifikasi dari widget Naive Bayes.

			[[File:NaiveBayes-visualize.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-03-06T21:17:35Z

Contoh

← Older revision		Revision as of 21:17, 6 March 2020
Line 22:		Line 22:
	==Contoh==		==Contoh==

	~~Here, we present two uses of this widget~~. ~~First~~, ~~we compare the results of the~~ Naive Bayes ~~with another~~ model, ~~the~~ Random Forest. We connect iris data ~~from~~ File to Test & Score. ~~We also~~ connect Naive Bayes ~~and~~ Random Forest to Test & Score ~~and observe their prediction scores~~.		Berikut ini kita akan memperlihatkan dua penggunaan dari Widget Naive Bayes. Pertama, kita akan membandingkan hasil dari Naive Bayes dengan model lain, yaitu Random Forest. Kita meng-connect iris data dari Widget File langsung ke Widget Test & Score. Kita juga meng-connect Widget Naive Bayes dan Widget Random Forest ke Widget Test & Score dan kita amati score prediksi-nya.

	[[File:NaiveBayes-classification.png\|center\|200px\|thumb]]		[[File:NaiveBayes-classification.png\|center\|200px\|thumb]]

	~~The second schema shows the quality of predictions made with~~ Naive Bayes. ~~We feed the~~ Test & Score ~~widget a~~ Naive Bayes learner ~~and then send the~~ data ~~to the~~ Confusion Matrix. ~~We also connect~~ Scatter Plot ~~with~~ File. ~~Then we select the misclassified instances in the~~ Confusion Matrix ~~and show feed them to~~ Scatter Plot. ~~The~~ bold ~~dots in the scatterplot are the misclassified instances from~~ Naive Bayes.		Skema kedua menunjukkan kualitas prediksi yang dihasilkan oleh Naive Bayes. Kita menyambungkan widget Test & Score dengan Naive Bayes learner dan kemudian mengirim data yang dihasilkan ke Confusion Matrix. Kami juga menghubungkan widget Scatter Plot dengan widget File. Kemudian kita memilih contoh-contoh kesalahan klasifikasi di widget Confusion Matrix dan menampilkannya ke widget Scatter Plot. Titik-titik bold di widget Scatter Plot adalah contoh kesalahan klasifikasi dari Naive Bayes.

	[[File:NaiveBayes-visualize.png\|center\|200px\|thumb]]		[[File:NaiveBayes-visualize.png\|center\|200px\|thumb]]

Onnowpurbo: /* Output */

2020-03-06T08:31:45Z

Output

← Older revision		Revision as of 08:31, 6 March 2020
Line 18:		Line 18:
	[[File:NaiveBayes-stamped.png\|center\|200px\|thumb]]		[[File:NaiveBayes-stamped.png\|center\|200px\|thumb]]

	~~This~~ widget ~~has two options: the name under which it will appear in other widgets and producing a~~ report. ~~The default name is~~ Naive Bayes. ~~When you change it~~, ~~you need to press~~ Apply.		Widget ini memiliki dua opsi: nama yang akan ditampilkan di widget lain dan menghasilkan report. Nama standarnya adalah Naive Bayes. Ketika kita mengubahnya, kita perlu menekan Apply.

	==Contoh==		==Contoh==

Onnowpurbo at 08:18, 6 March 2020

2020-03-06T08:18:39Z

← Older revision		Revision as of 08:18, 6 March 2020
Line 2:		Line 2:


			Klasifikasi probabilistik yang cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature.
	~~A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of~~ feature ~~independence~~.

	==Input==		==Input==
Line 15:		Line 14:
	Model: trained model		Model: trained model

	Naive Bayes ~~learns a~~ Naive Bayesian ~~model from the~~ data. ~~It only works for~~ classification ~~tasks~~.		Widget Naive Bayes mempelajari model Naive Bayesian dari data. Widget ini hanya berfungsi untuk task classification.

	[[File:NaiveBayes-stamped.png\|center\|200px\|thumb]]		[[File:NaiveBayes-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 03:58, 28 January 2020

2020-01-28T03:58:41Z

← Older revision		Revision as of 03:58, 28 January 2020
Line 5:		Line 5:
	A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of feature independence.		A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of feature independence.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

	~~Outputs~~		==Output==

	Learner: naive bayes learning algorithm		Learner: naive bayes learning algorithm
	Model: trained model		Model: trained model

	Naive Bayes learns a Naive Bayesian model from the data. It only works for classification tasks.		Naive Bayes learns a Naive Bayesian model from the data. It only works for classification tasks.

Onnowpurbo: /* Contoh */

2020-01-23T02:55:20Z

Contoh

← Older revision		Revision as of 02:55, 23 January 2020
Line 29:		Line 29:
	The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.		The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.

	File:NaiveBayes-visualize.png\|center\|200px\|thumb]]		[[File:NaiveBayes-visualize.png\|center\|200px\|thumb]]




	==Referensi==		==Referensi==

Onnowpurbo at 02:53, 23 January 2020

2020-01-23T02:53:07Z

← Older revision		Revision as of 02:53, 23 January 2020
Line 8:		Line 8:

	Data: input dataset		Data: input dataset

	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

Line 14:		Line 13:

	Learner: naive bayes learning algorithm		Learner: naive bayes learning algorithm

	Model: trained model		Model: trained model

	Naive Bayes learns a Naive Bayesian model from the data. It only works for classification tasks.		Naive Bayes learns a Naive Bayesian model from the data. It only works for classification tasks.

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

	This widget has two options: the name under which it will appear in other widgets and producing a report. The default name is Naive Bayes. When you change it, you need to press Apply.		This widget has two options: the name under which it will appear in other widgets and producing a report. The default name is Naive Bayes. When you change it, you need to press Apply.
	~~Examples~~
			==Contoh==

	Here, we present two uses of this widget. First, we compare the results of the Naive Bayes with another model, the Random Forest. We connect iris data from File to Test & Score. We also connect Naive Bayes and Random Forest to Test & Score and observe their prediction scores.		Here, we present two uses of this widget. First, we compare the results of the Naive Bayes with another model, the Random Forest. We connect iris data from File to Test & Score. We also connect Naive Bayes and Random Forest to Test & Score and observe their prediction scores.

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

	The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.		The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.

	~~../../_images/~~NaiveBayes-visualize.png		File:NaiveBayes-visualize.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/naivebayes.html A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption..."

2020-01-16T07:05:06Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/naivebayes.html A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/naivebayes.html

A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of feature independence.

Inputs

Data: input dataset

Preprocessor: preprocessing method(s)

Outputs

Learner: naive bayes learning algorithm

Model: trained model

Naive Bayes learns a Naive Bayesian model from the data. It only works for classification tasks.

../../_images/NaiveBayes-stamped.png

This widget has two options: the name under which it will appear in other widgets and producing a report. The default name is Naive Bayes. When you change it, you need to press Apply.
Examples

Here, we present two uses of this widget. First, we compare the results of the Naive Bayes with another model, the Random Forest. We connect iris data from File to Test & Score. We also connect Naive Bayes and Random Forest to Test & Score and observe their prediction scores.

../../_images/NaiveBayes-classification.png

The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.

../../_images/NaiveBayes-visualize.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/model/naivebayes.html

==Pranala Menarik==

* [[Orange]]