Dive deeper
9 articles
- Understanding Pecan’s BenchmarksBenchmarks evaluate ML models by comparing them to rule-based models, to understand their performance and communicate value to stakeholders
- What is data leakage and how can you prevent it?Avoid data leakage in ML models: Use only pre-prediction data to prevent "future peeking" and ensure valid, accurate outcomes.
- What is overfitting?Overfitting is when a model memorizes training data instead of learning patterns. Resolve it by reducing attributes and adding more data.
- What is underfitting?Underfitting occurs when a model fails to capture the underlying patterns in data, leading to poor performance on both training and new data
- How do you know if your model is good?To determine your model's performance, we compare its lift to random guess and benchmark models. You can also run A/B tests and outcomes.
- How to determine if your model is healthy?Think of Pecan's health checks as a model's doctor visit. From data diets to overfitting sniffles, we ensure your model is in tip-top shape!
- Understanding threshold logicThresholds in binary classification models balance precision & recall, affecting model performance. Adjust based on business needs & costs.
- Understanding Column importanceA peak into the "black box" of a model: Key to unlocking model insights and optimizing predictions by weighting features' impact on outcomes
- What is Label stability?Tackle Label Drift in ML models with Pecan: Detect shifts, adapt training, and monitor for precision in ever-evolving data landscapes.