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Evaluating a Model
Evaluating a Model

Understand your model's performance and metrics


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.