Get the important variables of random forest classifier

This example illustrates the extra analysis that random forest can provide for data scientists. In a random forest, we can rank the important features based on the error caused by dropping any of them.

[code2=python] #https://jupyter.org/try
#Demo7 - part2
#M. S. Rakha, Ph.D.
# Post-Doctoral - Queen's University
# Supervised Learning - Random Forest
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from
...

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Statistics : Posted by DrRakha • on Tue Oct 29, 2019 3:22 pm • Replies 0 • Views 620

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