Feature selection through Random Forest and Principal Component Analysis
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I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 principal loading vectors using magnitude of loading corresponding to the variable. This method fetched me 148 variables. I also picked top 148 variables from random forest basis Mean decrease in Gini but there is an overlap of only 22 variables in the two approaches. Is there a rationale to it?
machine-learning feature-selection feature-engineering
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SachinSachin
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I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 principal loading vectors using magnitude of loading corresponding to the variable. This method fetched me 148 variables. I also picked top 148 variables from random forest basis Mean decrease in Gini but there is an overlap of only 22 variables in the two approaches. Is there a rationale to it?
machine-learning feature-selection feature-engineering
asked 1 min ago
SachinSachin
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
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I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 principal loading vectors using magnitude of loading corresponding to the variable. This method fetched me 148 variables. I also picked top 148 variables from random forest basis Mean decrease in Gini but there is an overlap of only 22 variables in the two approaches. Is there a rationale to it?
machine-learning feature-selection feature-engineering
asked 1 min ago
SachinSachin
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working on a binary classification problem and I have 870 numeric independent features to start with. I tried PCA on input features and picked top 200 variables corresponding to first 10 principal loading vectors using magnitude of loading corresponding to the variable. This method fetched me 148 variables. I also picked top 148 variables from random forest basis Mean decrease in Gini but there is an overlap of only 22 variables in the two approaches. Is there a rationale to it?
machine-learning feature-selection feature-engineering
machine-learning feature-selection feature-engineering
asked 1 min ago
SachinSachin
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
SachinSachin
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
SachinSachin
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
SachinSachin
1
asked 1 min ago
SachinSachin
1
1
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sachin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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