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Benchmark Core Concepts

Understanding task types, metrics, and how benchmarks are executed is essential for effective model evaluation.

Task Types​

Benchmarks support different task types:

Classification​

Classification tasks predict discrete categories or classes.

Use Cases:

  • Fraud detection (fraud/not fraud)
  • Credit approval (approved/denied)
  • Customer segmentation
  • Sentiment analysis

Metrics:

  • Accuracy, Precision, Recall, F1
  • ROC AUC, Log Loss
  • KS Statistic, Gini Coefficient

Regression​

Regression tasks predict continuous numerical values.

Use Cases:

  • Price prediction
  • Demand forecasting
  • Risk scoring
  • Revenue prediction

Metrics:

  • MSE, MAE, RMSE
  • R-squared (R²)
  • Mean Absolute Percentage Error

Text Generation​

Text generation tasks generate text sequences.

Use Cases:

  • Text completion
  • Content generation
  • Summarization
  • Translation

Metrics:

  • Perplexity
  • BLEU score
  • ROUGE score
  • Custom metrics

Metrics Overview​

Benchmarks support various metrics for different task types:

Classification Metrics:

  • Accuracy, Precision, Recall, F1
  • ROC AUC, Log Loss
  • KS Statistic, Gini Coefficient

Regression Metrics:

  • MSE, MAE, RMSE
  • R-squared (R²)

Metric Selection:

  • Choose metrics appropriate for your task
  • Consider business requirements
  • Use multiple metrics for comprehensive evaluation
  • Understand metric interpretations

Dataset Consistency​

Ensure benchmark datasets remain consistent:

Consistency Requirements:

  • Fixed Schema: Schema should not change
  • Fixed Features: Feature set should remain constant
  • Fixed Partitioning: Partitioning should be reproducible
  • Version Control: Track dataset versions

Consistency Benefits:

  • Fair model comparison
  • Reproducible results
  • Reliable performance tracking
  • Valid performance trends

Benchmark Execution​

How benchmarks are executed:

Execution Process:

  1. Select Benchmark: Choose benchmark to execute
  2. Select Model: Choose model to evaluate
  3. Run Evaluation: Execute benchmark evaluation
  4. Collect Results: Collect evaluation results
  5. Compare Results: Compare with other models

Execution Features:

  • Automated: Automatic evaluation execution
  • Reproducible: Reproducible evaluation results
  • Scalable: Handle large datasets efficiently
  • Tracked: Track all evaluation runs

Next Steps​