🎓 Advanced Machine Learning Series

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🎓 Advanced Machine Learning Series – Part 4: Real-World Applications & Model Comparison

This is the final part of the Advanced Machine Learning series on Darchumstech. Here we explore how ensemble methods are used in real-world scenarios and how to compare different ensemble models effectively.

Ensemble models are widely used across industries due to their accuracy and robustness:

  • Finance: Credit scoring, fraud detection.
  • Healthcare: Disease prediction from lab data.
  • Retail: Churn prediction and recommendations.
  • Marketing: Campaign response modeling.

Important metrics when comparing models:

  • Accuracy: Overall correctness.
  • Precision & Recall: For imbalanced data.
  • ROC AUC: Threshold-independent metric.
  • Training Time: Boosting takes longer than bagging.

Always use cross-validation for consistent results.

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)

xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
xgb.fit(X_train, y_train)
xgb_pred = xgb.predict(X_test)

print("Random Forest Accuracy:", accuracy_score(y_test, rf_pred))
print("XGBoost Accuracy:", accuracy_score(y_test, xgb_pred))

Compare bagging vs boosting for breast cancer prediction accuracy.



  







  
  



















































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