Pecan

Benchmarks evaluate ML models by comparing them to rule-based models, to understand their performance and communicate value to stakeholders

Understanding Pecan’s Benchmarks

Entity-level explainability is a great tool for understanding and interpreting ML models, improving them and even help finding errors.

Overfitting is when a model memorizes training data instead of learning patterns. Resolve it by reducing attributes and adding more data.

Underfitting occurs when a model fails to capture the underlying patterns in data, leading to poor performance on both training and new data

Understand your model's performance and metrics

Pecan's dashboard offers accuracy insights & customization, empowering you to optimize model performance & surpass benchmarks.

Binary classification models use confusion matrix to calculate precision & recall rates, determining model effectiveness & accuracy.

Thresholds in binary classification models balance precision & recall, affecting model performance. Adjust based on business needs & costs.

Pecan legacy dashboard is still available if you wish to use it.

Find answers and get help from Intercom Support and Community Experts

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