Orange: Naive Bayes - Revision history

Onnowpurbo at 10:30, 5 April 2020

2020-04-05T10:30:01Z

Onnowpurbo: /* Contoh */

2020-03-06T21:17:35Z

Contoh

Onnowpurbo: /* Output */

2020-03-06T08:31:45Z

Output

Onnowpurbo at 08:18, 6 March 2020

2020-03-06T08:18:39Z

Onnowpurbo at 03:58, 28 January 2020

2020-01-28T03:58:41Z

Onnowpurbo: /* Contoh */

2020-01-23T02:55:20Z

Contoh

Onnowpurbo at 02:53, 23 January 2020

2020-01-23T02:53:07Z

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]]

@@ 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==
@@ Line 14: / Line 14: @@
   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==
-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.
 ==Referensi==

@@ Line 22: / Line 22: @@
 ==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]]
-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]]

@@ Line 18: / Line 18: @@
 [[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==

@@ Line 2: / Line 2: @@
+Klasifikasi probabilistik yang cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature.
 ==Input==
@@ Line 15: / Line 14: @@
   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]]

@@ 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.
-File:NaiveBayes-visualize.png|center|200px|thumb]]
+[[File:NaiveBayes-visualize.png|center|200px|thumb]]
 ==Referensi==

@@ Line 5: / Line 5: @@
 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.