Difference between revisions of "Orange: Correlations"

Latest revision as of 09:58, 20 April 2020

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

Widget Correlations meng-hitung semua korelasi atribut berpasangan.

Input

Data: input dataset

Output

Data: input dataset
Features: selected pair of features
Correlations: data table with correlation scores

Widget Correlations menghitung skor korelasi Pearson atau Spearman untuk semua pasangan fitur dalam dataset. Metode-metode ini hanya dapat mendeteksi hubungan monoton.

Correlation measure:
- Pairwise Pearson correlation.
- Pairwise Spearman correlation.
Filter for finding attribute pairs.
A list of attribute pairs with correlation coefficient. Press Finished to stop computation for large datasets.
Access widget help and produce report.

Contoh

Widget Correlations menghitung korelasi hanya untuk feature numerik (continuous), jadi kita akan menggunakan housing sebagai contoh dataset. Load dataset housing menggunakan widget File dan hubungkan ke widget Correlations. Pasangan fitur yang berkorelasi positif akan berada di bagian atas daftar dan berkorelasi negatif akan berada di bagian bawah.

Untuk mencek pasangan yang paling berkorelasi negatif, DIS-NOX. Sekarang hubungkan widget Scatter Plot ke widget Correlations dan atur dua output, Data ke Data dan Feature ke Feature. Amati bagaimana pasangan fitur segera di set dalam widget Scatter Plot. Sepertinya kedua fitur tersebut memang berkorelasi negatif.

Referensi

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

Pranala Menarik

Orange

@@ Line 2: / Line 2: @@
-Compute all pairwise attribute correlations.
+Widget Correlations meng-hitung semua korelasi atribut berpasangan.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-Outputs
+==Output==
-    Data: input dataset
+ Data: input dataset
+ Features: selected pair of features
+ Correlations: data table with correlation scores
-    Features: selected pair of features
+Widget Correlations menghitung skor korelasi Pearson atau Spearman untuk semua pasangan fitur dalam dataset. Metode-metode ini hanya dapat mendeteksi hubungan monoton.
-    Correlations: data table with correlation scores
+[[File:Correlations-stamped.png|center|200px|thumb]]
-Correlations computes Pearson or Spearman correlation scores for all pairs of features in a dataset. These methods can only detect monotonic relationship.
-../../_images/Correlations-stamped.png
+* Correlation measure:
+** Pairwise Pearson correlation.
+** Pairwise Spearman correlation.
+* Filter for finding attribute pairs.
+* A list of attribute pairs with correlation coefficient. Press Finished to stop computation for large datasets.
+* Access widget help and produce report.
-    Correlation measure:
+==Contoh==
-        Pairwise Pearson correlation.
+Widget Correlations menghitung korelasi hanya untuk feature numerik (continuous), jadi kita akan menggunakan housing sebagai contoh dataset. Load dataset housing menggunakan widget File dan hubungkan ke widget Correlations. Pasangan fitur yang berkorelasi positif akan berada di bagian atas daftar dan berkorelasi negatif akan berada di bagian bawah.
-        Pairwise Spearman correlation.
+[[File:Correlations-links.png|center|400px|thumb]]
-    Filter for finding attribute pairs.
-    A list of attribute pairs with correlation coefficient. Press Finished to stop computation for large datasets.
-    Access widget help and produce report.
-Example
-Correlations can be computed only for numeric (continuous) features, so we will use housing as an example data set. Load it in the File widget and connect it to Correlations. Positively correlated feature pairs will be at the top of the list and negatively correlated will be at the bottom.
-../../_images/Correlations-links.png
-Go to the most negatively correlated pair, DIS-NOX. Now connect Scatter Plot to Correlations and set two outputs, Data to Data and Features to Features. Observe how the feature pair is immediately set in the scatter plot. Looks like the two features are indeed negatively correlated.
-../../_images/Correlations-Example.png
+Untuk mencek pasangan yang paling berkorelasi negatif, DIS-NOX. Sekarang hubungkan widget Scatter Plot ke widget Correlations dan atur dua output, Data ke Data dan Feature ke Feature. Amati bagaimana pasangan fitur segera di set dalam widget Scatter Plot. Sepertinya kedua fitur tersebut memang berkorelasi negatif.
+[[File:Correlations-Example.png|center|600px|thumb]]
 ==Referensi==