please code in python Build the pipeline that uses a Principal Component Analysis (PCA) model to extract 2 principal components of the training set and create a a Random Forest model that consists of 50 base decision trees (same as the model in 2D). Fill in the myPCARF function, which accepts as input the training set and returns a fully trained model. Template: def myPCARF(Xtrain, ytrain): #write function here return myPCARF You can use any sample dataset
please code in python Build the pipeline that uses a Principal Component Analysis (PCA) model to extract 2 principal components of the training set and create a a Random Forest model that consists of 50 base decision trees (same as the model in 2D). Fill in the myPCARF function, which accepts as input the training set and returns a fully trained model. Template: def myPCARF(Xtrain, ytrain): #write function here return myPCARF You can use any sample dataset
C++ Programming: From Problem Analysis to Program Design
8th Edition
ISBN:9781337102087
Author:D. S. Malik
Publisher:D. S. Malik
Chapter17: Linked Lists
Section: Chapter Questions
Problem 2PE
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please code in python
Build the pipeline that uses a Principal Component Analysis (PCA) model to extract 2 principal components of the training set and create a a Random Forest model that consists of 50 base decision trees (same as the model in 2D). Fill in the myPCARF function, which accepts as input the training set and returns a fully trained model.
Template:
def myPCARF(Xtrain, ytrain):
#write function here
return myPCARF
You can use any sample dataset
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