Adding more features improves the variance explained by PCA but the prediction model performs worse
52047
How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?
55847
PCA demeaning the data
14183
Why Lasso or ElasticNet perform better than Ridge when the features are correlated
24814
Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
18487
Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
98728
Random forest is overfitting?
125575
Dimensional regularization: removing more than just logarithmic divergencies?
154757
One component in PCA is always the mean vector in two-dimensions but not three
16641
Strange results of varimax rotation of principal component analysis in Stata: rotated components are all zeros and ones
73022
Sklearn ValueError: X has 2 features per sample; expecting 11
102983
(Why) do overfitted models tend to have large coefficients?
51084
Won't highly-correlated variables in random forest distort accuracy and feature-selection?
105175
Variable ordering using PCA
145342
Why do the eigenvectors for two similar matrixes differ by a large amount
103877
The advantages and disadvantages of using Kaiser Rule to select the number of principal components
156166
PCA of non-Gaussian data
80801
Why does the variance of the Random walk increase?