Analysis result of PCA
hi
I want to use PCA.
But I can't analyze the results.
Because the column names are displayed as "PC_1". And I don't know which feature the "PC_1" belongs to.
please help me.
thanks.
Because the column names are displayed as "PC_1". And I don't know which feature the "PC_1" belongs to.
please help me.
thanks.
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One Another question;Isn't the label feature used in PCA?
Hi,
this is how PCA works. It calculates new principal components (PC_1 to PC_n) from the original attributes.
If you need the original attributes in addition to the principal components, you can do the following:
1. Have or generate a unique ID in your data
2. Set Role of this ID attribute to id so PCA doesn't change it
3. Multiply
4. Execute the PCA on one of the branches
5. Join the processing branches back based on the ID attribute
If you mark an attribute as label (with Set Role), the PCA operator won't change it. This allows you to build a model on the PCA attributes.
I hope this helps. Here's a PCA video in the RapidMiner Academy:
https://academy.rapidminer.com/learn/video/workshop-pca
Best regards,
Balázs
this is how PCA works. It calculates new principal components (PC_1 to PC_n) from the original attributes.
If you need the original attributes in addition to the principal components, you can do the following:
1. Have or generate a unique ID in your data
2. Set Role of this ID attribute to id so PCA doesn't change it
3. Multiply
4. Execute the PCA on one of the branches
5. Join the processing branches back based on the ID attribute
If you mark an attribute as label (with Set Role), the PCA operator won't change it. This allows you to build a model on the PCA attributes.
I hope this helps. Here's a PCA video in the RapidMiner Academy:
https://academy.rapidminer.com/learn/video/workshop-pca
Best regards,
Balázs
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Hi,
this is how PCA works. It calculates new principal components (PC_1 to PC_n) from the original attributes.
If you need the original attributes in addition to the principal components, you can do the following:
1. Have or generate a unique ID in your data
2. Set Role of this ID attribute to id so PCA doesn't change it
3. Multiply
4. Execute the PCA on one of the branches
5. Join the processing branches back based on the ID attribute
If you mark an attribute as label (with Set Role), the PCA operator won't change it. This allows you to build a model on the PCA attributes.
I hope this helps. Here's a PCA video in the RapidMiner Academy:
https://academy.rapidminer.com/learn/video/workshop-pca
Best regards,
Balázs
this is how PCA works. It calculates new principal components (PC_1 to PC_n) from the original attributes.
If you need the original attributes in addition to the principal components, you can do the following:
1. Have or generate a unique ID in your data
2. Set Role of this ID attribute to id so PCA doesn't change it
3. Multiply
4. Execute the PCA on one of the branches
5. Join the processing branches back based on the ID attribute
If you mark an attribute as label (with Set Role), the PCA operator won't change it. This allows you to build a model on the PCA attributes.
I hope this helps. Here's a PCA video in the RapidMiner Academy:
https://academy.rapidminer.com/learn/video/workshop-pca
Best regards,
Balázs