Using resource-heavy classifiers like RandomForestClassifier for standard classification tasks can result in longer execution times and higher carbon emissions.

XGBoost offers a more optimized and eco-friendly alternative with competitive accuracy.

Metrics Comparison Table

Classifier	Accuracy	Time (s)	Carbon Emission (kg CO₂)
RandomForestClassifier	0.88	1.24	0.00091
XGBClassifier	0.89	0.47	0.00035

XGBoost not only matches or exceeds the accuracy of RandomForest but also runs significantly faster and emits less CO₂.

Visual Comparison

Accuracy vs Dataset Size

Carbon Emission vs Dataset Size

Execution Time vs Dataset Size

Non Compliant Code Example

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

Compliant Code Example

from xgboost import XGBClassifier

model = XGBClassifier(eval_metric="logloss")
model.fit(X_train, y_train)

📓 Article about XGBoost: A Scalable Tree Boosting System

This article explains in detail what XGBoost can do : https://arxiv.org/pdf/1603.02754