ModelEvaluator Class
* Comprehensive replacement for the older model_metrics function
* Supports a wide variety of classification and regression models, including multi-class problems
* Extensive visualizations (ROC, PR curves, lift charts, SHAP values)
* Detailed metrics and performance analytics in a single, cohesive interface
* Ability to add custom metrics, save plots, and export metrics to file
Apply Functions
* New functions to consistently transform new data using patterns from training:
* apply_outliers() - Apply existing outlier limits
* apply_missing_values() - Apply missing value handling
* apply_dummy() - Apply existing dummy coding
* These enable production pipelines to use identical transformations to training
* Don't require building a separate .py file for scoring transformations - all transformations are handled directly in the configuration yaml file
ConfigGenerator Class
* Automatically creates scoring configuration files modularly
* Supports nested parameters and complex configuration structures
* Perfect for version controlling your model parameters and preprocessing steps
Memory Optimization
* New memory_optimization() function dramatically reduces DataFrame memory usage
* Significantly reduces time to train XGBoost models by taking advantage of sparse arrays
* Configurable precision modes to balance memory usage vs. numeric precision
Other New Functions Added
* generate_sql_trend_query() - Generate SQL for time-period analysis
* trend_analysis() - Analyze time-series data for patterns
Other Notable Improvements
* Consistent return patterns (functions now return both data AND metadata)
* Standardized function names and improved parameter handling
* More robust outlier detection with skew adjustment options
* Better missing value handling with more filling methods
* Enhanced dummy coding with better prefix handling
* Improved correlation/feature reduction with multiple correlation methods
* Enhanced split_data() with stratification options and better sampling
Breaking Changes
* Many of the calls prior to 2.0.0 will not work correctly without slight modifications. Consult the documentation for exact changes.
* inplace parameters removed from all functions to conform with pandas best practices
* missing_fill() and missing_check() combined into missing_values()
* dv_proxies() renamed to remove_outcome_proxies() for better clarity
* memory_usage() renamed to memory_optimization