Advanced Statistical Analysis
Last updated September 6, 2024
While Evidently AI provides intuitive visualizations and basic statistical insights, it can be further extended to perform deeper statistical analysis for a more comprehensive understanding of data drift, model performance changes, and trends.
Advanced Statistical Analysis with Evidently AI:
- Leverage the API: Utilize the Evidently AI API to access the raw data and statistical calculations from the reports. This gives you greater control over the analysis and the ability to implement custom statistical methods.
- Custom Metrics: Define custom metrics using the `Metric` class to calculate more advanced statistical measures. These can range from specific statistical tests to complex combinations of calculations.
- Hypothesis Testing: Use the API to implement statistical hypothesis testing to determine if observed changes in data or model performance are statistically significant or due to random fluctuations.
- Trend Analysis: Use statistical tools to identify trends in your data over time. This might involve calculating rolling averages, performing time series analysis, or fitting statistical models to identify patterns or anomalies.
- Data Distributions: Dive deeper into the analysis of data distributions. Use statistical tests like the Kolmogorov-Smirnov test to compare the distributions of features in the reference and current data and identify significant differences.
- Correlation Analysis: Use statistical methods like Pearson or Spearman correlation to explore relationships between features in your data. Identify strong correlations that can indicate potential issues or insights.
- Machine Learning Models: Leverage the API to apply machine learning models (e.g., clustering, classification) to understand the underlying patterns and structure in your data. This can help detect subtle data drift or identify potential issues related to model bias or fairness.
- Example (Hypothese testing using the API):
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