Using RandomForestClassifier in sklearn for Unbalanced Classification in Python

In this article, we will introduce how to use the RandomForestClassifier algorithm in the sklearn library in Python to perform unbalanced classification. Unbalanced classification refers to a situation where the number of samples in different classes in a dataset varies significantly, which is common in real-world applications. This article will use an example to illustrate how to deal with unbalanced classification problems and use RandomForestClassifier to perform classification predictions.

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What is Unbalanced Classification

Unbalanced classification refers to a situation where the number of samples in different classes in a dataset varies significantly. We often encounter such situations in real life, such as identifying rare cases in medical diagnosis and fraudulent transactions in credit card fraud detection. In imbalanced classification problems, due to the imbalance between classes, traditional classification algorithms tend to favor predictions for the more numerous class and perform poorly for the less numerous class.

How to Deal with Imbalanced Classification Problems

For imbalanced classification problems, we can use the following methods to improve the performance of classification models:

1. Data Resampling

Data resampling balances the dataset by increasing the number of minority class samples or decreasing the number of majority class samples. Common methods include undersampling and oversampling. Undersampling reduces dataset imbalance by removing majority class samples, such as random undersampling and cluster undersampling. Oversampling increases dataset imbalance by replicating minority class samples, such as random oversampling and SMOTE oversampling.

2. Class Weight Adjustment

Class weight adjustment balances the dataset by assigning different weights to samples in different classes. In the sklearn library, the RandomForestClassifier algorithm provides a function for adjusting class weights using the class_weight parameter. We can adjust the weights based on the class ratio, causing the model to prioritize samples from the minority class during training.

3. Threshold Adjustment

Threshold adjustment balances the dataset by adjusting the classifier’s threshold for different classes. In traditional binary classification problems, the default threshold is 0.5, meaning that a predicted probability greater than 0.5 is considered a positive example, and a probability less than 0.5 is considered a negative example. For imbalanced classification problems, we can adjust the threshold to achieve a balanced classification.

Using RandomForestClassifier for Unbalanced Classification

Next, we will use the RandomForestClassifier algorithm from the sklearn library in Python to perform unbalanced classification.

First, we need to import the necessary libraries and modules and prepare our dataset. Suppose our dataset is for predicting customer churn in a bank, where positive examples represent churned customers and negative examples represent those who did not churn. We load the dataset from a CSV file and split it into features and labels.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Import the dataset
data = pd.read_csv('churn.csv')

# Split features and labels
X = data.drop('Churn', axis=1)
y = data['Churn']

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Next, we need to address the imbalanced classification problem. We can balance the dataset using class weighting. In the RandomForestClassifier algorithm, we can set class weights using the class_weight parameter. We can set weights based on the ratio of positive to negative examples in the training set, causing the model to prioritize the minority class during training.

# Calculate class weights
class_weights = dict(data['Churn'].value_counts(normalize=True))

# Create a RandomForestClassifier model
model = RandomForestClassifier(n_estimators=100, class_weight=class_weights)

# Train the model
model.fit(X_train, y_train)

After training, we can evaluate the model’s performance using the test set. We can use the classification_report function to generate a classification report, which includes metrics such as the model’s precision, recall, and F1 score for different categories.

# Predict the test set
y_pred = model.predict(X_test)

# Generate a classification report
report = classification_report(y_test, y_pred)
print(report)

Summary

This article introduced how to use the RandomForestClassifier algorithm in the sklearn library in Python for unbalanced classification. For unbalanced classification problems, we can improve the performance of the classification model using methods such as data resampling, class weight adjustment, and threshold adjustment. Specifically, we can use the class_weight parameter of the RandomForestClassifier algorithm to adjust class weights and the classification_report function to evaluate the model’s performance on different classes. In practice, we choose the appropriate method to handle unbalanced classification problems based on the specific problem and dataset, and select the optimal classification model and tuning method based on the evaluation metrics.