Evaluating a Model
Understand your model's performance and metrics
21 articles
- Dashboard Overview For Binary ModelsPecan's dashboard offers accuracy insights & customization, empowering you to optimize model performance & surpass benchmarks.
- Evaluation metrics in binary modelsBinary classification models use confusion matrix to calculate precision & recall rates, determining model effectiveness & accuracy.
- Understanding Probability ScoreBinary classification in ML: Models predict entity classes with probability scores, and thresholds customize predictions for business needs.
- Dashboard Overview For Regression ModelsExplore your Pecan AI model's insights with a dashboard: track performance, compare predictions, analyze feature importance, and more!
- Model performance metrics for regression modelsMaster regression model evaluation with Pecan's diverse metrics: MdAPE, MAPE, WMAPE, WMPE, R2, and more. Precision in every prediction!
- Outliers Alert in Dashboards (Regression Models)Spot outliers in regression models, clip them, and improve predictions with Pecan’s Outliers Alert
- 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.
- Maintaining Feature Balance in Machine Learning ModelsFeature or column importance in ML models gauges predictor significance.
- Understanding Explainability & Prediction DetailsEntity-level explainability is a great tool for understanding and interpreting ML models, improving them and even help finding errors.
- SHAP valuesSHAP values quantify feature impact in ML models, revealing key drivers in predictions and aiding in data-driven decision-making.
- Model performance metrics for binary modelsLearn about binary model metrics: Base Rate, Precision, Detection, AUC, LogLoss guide accurate, balanced predictions for distinct classes.