Contents
Can a random forest be trained with PCA?
Yes, PCA can make i nterpreting each “feature” a little harder when we analyze the “feature importances” of our Random Forest model. However, PCA performs dimensionality reduction, which can reduce the number of features for the Random Forest to process, so PCA might help speed up the training of your Random Forest model.
How are feature / variable importance after a PCA analysis?
I have performed a PCA analysis over my original dataset and from the compressed dataset transformed by the PCA I have also selected the number of PC I want to keep (they explain almost the 94% of the variance). Now I am struggling with the identification of the original features that are important in the reduced dataset.
How is variable importance calculated for a random forest?
There are two measures of importance given for each variable in the random forest. The first measure is based on how much the accuracy decreases when the variable is excluded. This is further broken down by outcome class.
What makes a feature important in a random forest?
The feature importance is the difference between the benchmark score and the one from the modified (permuted) dataset. Repeat 2. for all features in the dataset. no need to retrain the model at each modification of the dataset
What are the drawbacks of random forests?
Note that computational cost is one of the biggest drawbacks of Random Forests (it can take a long time to run the model). PCA can become really important especially when you are working with hundreds or even thousands of predicting features.
How to apply random forest to test data?
Then I am training the model using RandomForestClassifier. Now, I am confused how to apply that trained model on the test data as the number of columns of test data and the retained transformed data (on which random forest is applied) is different. Any solution would be appreciated.
How to apply PCA to machine learning problem?
In solving one of the machine learning problem, I am implementing PCA on training data and and then applying .transform on train data using sklearn. After observing the variances, I retain only those columns from the transformed data whose variance is large.