Orange: Randomize: Difference between revisions

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Shuffles classes, attributes and/or metas of an input dataset.
Mengacak class, attribute dan/atau meta dari input dataset.


==Input==
==Input==
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  Data: randomized dataset
  Data: randomized dataset


The Randomize widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.
Randomize widget menerima dataset pada input dan akan meng-output dataset yang sama di mana kelas, atribut atau / dan meta diacak.


[[File:Randomize-Default.png|center|200px|thumb]]
[[File:Randomize-Default.png|center|200px|thumb]]


 
* Select group of columns of the dataset you want to shuffle.
    Select group of columns of the dataset you want to shuffle.
* Select proportion of the dataset you want to shuffle.
    Select proportion of the dataset you want to shuffle.
* Produce replicable output.
    Produce replicable output.
* If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.
    If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.
* Hasilkan Report.
    Produce a report.


==Contoh==
==Contoh==


The Randomize widget is usually placed right after (e.g. File widget. The basic usage is shown in the following workflow, where values of class variable of Iris dataset are randomly shuffled.
Randomize widget biasanya ditempatkan tepat setelahnya (mis. File widget). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.


[[File:Randomize-Example1.png|center|200px|thumb]]
[[File:Randomize-Example1.png|center|200px|thumb]]


In the next example we show how shuffling class values influences model performance on the same dataset as above.
Dalam contoh berikut, kita menunjukkan bagaimana pengocokan nilai class mempengaruhi kinerja model pada dataset yang sama seperti di atas.


[[File:Randomize-Example2.png|center|200px|thumb]]
[[File:Randomize-Example2.png|center|200px|thumb]]

Revision as of 04:50, 27 January 2020

Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/randomize.html


Mengacak class, attribute dan/atau meta dari input dataset.

Input

Data: input dataset

Output

Data: randomized dataset

Randomize widget menerima dataset pada input dan akan meng-output dataset yang sama di mana kelas, atribut atau / dan meta diacak.

  • Select group of columns of the dataset you want to shuffle.
  • Select proportion of the dataset you want to shuffle.
  • Produce replicable output.
  • If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.
  • Hasilkan Report.

Contoh

Randomize widget biasanya ditempatkan tepat setelahnya (mis. File widget). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.

Dalam contoh berikut, kita menunjukkan bagaimana pengocokan nilai class mempengaruhi kinerja model pada dataset yang sama seperti di atas.



Referensi

Pranala Menarik

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