I have 4 predictive features and 4800 observations. I did PCA fit like this:
pca = PCA(n_components=X_train_scaled.shape,whiten=True) pca.fit(X_train_scaled)
and I got
I have a few question regarding this:
Does this mean that the first feature, which is
age in this case, is the most important?
I know that this is the variance of principle components, but why are there 4 PCs just like the features? what can I infer from this plot about my predictive features?
In other words, how can this help me in the down-stream analysis in regards to choosing maybe the best features from the 4?