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💡[Feature]: Classification of Arrhythmia [ECG DATA] || ML #214
Algorithms Used
KNN Classifier
Logestic Regression
Decision Tree Classifier
Linear SVC
Kernelized SVC
Random Forest Classifier
Principal Component analysis (PCA)
Use Case
The models started performing better after we applied PCA on the resampled data. The reason behind this is, PCA reduces the complexity of the data. It creates components based on giving importance to variables with large variance and also the components which it creates are non collinear in nature which means it takes care of collinearity in large data set. PCA also improves the overall execution time and quality of the models and it is very beneficial when we are working with huge amount of variables.
ashis2004
commented
5 months ago
Is there an existing issue for this?
Feature Description
Algorithms Used KNN Classifier Logestic Regression Decision Tree Classifier Linear SVC Kernelized SVC Random Forest Classifier Principal Component analysis (PCA)
Use Case
The models started performing better after we applied PCA on the resampled data. The reason behind this is, PCA reduces the complexity of the data. It creates components based on giving importance to variables with large variance and also the components which it creates are non collinear in nature which means it takes care of collinearity in large data set. PCA also improves the overall execution time and quality of the models and it is very beneficial when we are working with huge amount of variables.
Benefits
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Priority
High
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