query: PCA before Random Forest Regression provide better predictive scores for my dataset than just Random Forest Regression, how to explain it?
positive_passages.docid: 52047
positive_passages.text: How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?
negative_passages.docid: 55662
negative_passages.text: How to use principal components analysis to select variables for regression?
negative_passages.docid: 6590
negative_passages.text: PCA and proportion of variance explained
negative_passages.docid: 98728
negative_passages.text: Random forest is overfitting?
negative_passages.docid: 36565
negative_passages.text: Box-Cox transformation vs. predictor transformation in multiple linear regression
negative_passages.docid: 97758
negative_passages.text: Is using correlation matrix to select predictors for regression correct?
negative_passages.docid: 121252
negative_passages.text: Interpreting output of importance of a random forest object in R
negative_passages.docid: 58047
negative_passages.text: Decision Trees and Regression - Can predicted values be outside range of training data?
negative_passages.docid: 116179
negative_passages.text: Is principal component regression (PCR) using principal component scores for regression?
negative_passages.docid: 75278
negative_passages.text: What is the effect of having correlated predictors in a multiple regression model?
negative_passages.docid: 114794
negative_passages.text: Is there any advantage of SVD over PCA?
negative_passages.docid: 95663
negative_passages.text: How to interpret Mean Decrease in Accuracy and Mean Decrease GINI in Random Forest models
negative_passages.docid: 85031
negative_passages.text: How to interpret this PCA biplot to determine which attributes to pick?
negative_passages.docid: 24814
negative_passages.text: Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
negative_passages.docid: 14465
negative_passages.text: "Normalizing" variables for SVD / PCA
negative_passages.docid: 10466
negative_passages.text: Making sense of principal component analysis, eigenvectors & eigenvalues
negative_passages.docid: 95969
negative_passages.text: How to choose the split in Random forest for categorical predictors (features)?