Handle Imbalanced Classes In Random Forest


# 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. Specifically:

$$w_j = \frac{n}{kn_{j}}$$

where $w_j$ is the weight to class $j$, $n$ is the number of observations, $n_j$ is the number of observations in class $j$, and $k$ is the total number of classes.

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

# Train model
model = clf.fit(X, y)