How can preprocessing with PCA but keeping the same dimensionality improve random forest results?
52047
How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?
24814
Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
157062
Dimensionality reduction (SVD or PCA) on a large, sparse matrix
3589
How to reverse PCA and reconstruct original variables from several principal components?
97435
Reversing PCA back to the original variables
102449
Weighting features prior to SVM
13076
Is random forest a boosting algorithm?
69637
Random Forests and data transformations
143724
Is it possible to use kernel PCA for feature selection?
51254
How to project a new vector onto the PC space using kernel PCA?
90482
Random forest regression not predicting higher than training data
101661
What should be the optimal parameters for Random Forest classifier?
67557
Imputation of missing values for PCA
95663
How to interpret Mean Decrease in Accuracy and Mean Decrease GINI in Random Forest models
113346
What is the proper association measure of a variable with a PCA component (on a biplot / loading plot)?
26181
Using principal component analysis (PCA) for feature selection
88083
How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?