Training Log Entry for Random Forest Model (Epoch 10)

Model: Random Forest Classifier
Epoch: 10
Dataset: Customer Churn Dataset
Training Phase: Model Training

Training Summary:

• Number of Trees: 100
• Maximum Depth: 10
• Features Used: 15 features
• Samples Used: 10,000
• Training Accuracy: 94.5%
• Validation Accuracy: 92.3%
• Training Loss: 0.32
• Validation Loss: 0.36

Metrics:

• Precision: 91.2%
• Recall: 89.8%
• F1-Score: 90.5%
• AUC (Area Under Curve): 0.94

Model Evaluation:

• The model shows consistent improvement in performance, with a slight drop in validation loss compared to the previous epoch.
• Precision and recall values remain balanced, with a focus on improving recall without sacrificing precision.
• The AUC indicates good separability between churned and non-churned customers.

Observations:

• The model’s performance is stabilizing after 10 epochs, with minimal overfitting as indicated by the training and validation metrics being closely aligned.
• No significant changes in feature importance have been observed since the earlier epochs. Key features driving the model’s predictions remain consistent.
• The model’s training time per epoch is approximately 15 minutes, and no significant performance degradation has been noted.

Next Steps:

• Monitor the training process over the next few epochs to ensure continued improvement in recall and precision.
• Consider hyperparameter tuning for max_depth and min_samples_split to further optimize performance.
• Perform additional validation on a holdout set to check for model generalization.