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Machine Learning for Beginners in Python: How to Handle Imbalanced Classes In Random Forest

By on Tuesday, May 25, 2021

Handle Imbalanced Classes In Random Forest

Preliminaries


/* Load libraries */
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from sklearn import datasets

Load Iris Flower Dataset


/* Load data */
iris = datasets.load_iris()
X = iris.data
y = iris.target

Adjust Iris Dataset To Make Classes Imbalanced


/* Make class highly imbalanced by removing first 40 observations */
X = X[40:,:]
y = y[40:]

/* Create target vector indicating if class 0, otherwise 1 */
y = np.where((y == 0), 0, 1)

Train Random Forest While Balancing Classes

When using RandomForestClassifier a useful setting is class_weight=balanced wherein classes are automatically weighted inversely proportional to how frequently they appear in the data.


/* Create decision tree classifer object */
clf = RandomForestClassifier(random_state=0, n_jobs=-1, class_weight="balanced")

/* Train model */
model = clf.fit(X, y)

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

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  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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