📢Day 12/100: Comparing Machine Learning Models
Today, I compared the performance of multiple machine learning models for credit scoring:
1️⃣ Logistic Regression: Simple and interpretable but less effective with complex data.
2️⃣ Random Forest: Excellent for feature importance but slower for large datasets.
3️⃣ Gradient Boosting: Best overall performance with high accuracy and recall.
💡 Finding: Gradient Boosting stood out with an ROC-AUC of 0.97.
💡 Question: Do you prioritize interpretability or accuracy when selecting a model for financial applications?
#MachineLearning #ModelSelection #CreditScoring #FintechEthiopia
Today, I compared the performance of multiple machine learning models for credit scoring:
1️⃣ Logistic Regression: Simple and interpretable but less effective with complex data.
2️⃣ Random Forest: Excellent for feature importance but slower for large datasets.
3️⃣ Gradient Boosting: Best overall performance with high accuracy and recall.
💡 Finding: Gradient Boosting stood out with an ROC-AUC of 0.97.
💡 Question: Do you prioritize interpretability or accuracy when selecting a model for financial applications?
#MachineLearning #ModelSelection #CreditScoring #FintechEthiopia