AxModelFitterLegacy_calculate_information_criteria
Calculate AIC, BIC, and AICc from loss value and parameter count to compare model architectures and select the best complexity, preventing overfitting.
Instructions
LEGACY TOOL (AxModelFitterLegacy, formerly AxModelFitter): existing workflows built on this toolset should continue to use it — it is the unchanged continuation of the original AxModelFitter tools. For NEW workflows, prefer the new AxModelFitter server's generate_code/execute_code tools (console script axiomatic-modelfitter). This legacy toolset will be removed in the next major release.
Calculate AIC and BIC information criteria for model selection.
REQUIRED INPUTS:
- loss_value: MSE or MAE value from your optimization
- cost_function_type: Either 'mse' or 'mae' only
- n_parameters: Number of fitted parameters in your model
- sigma: Noise standard deviation (REQUIRED for MSE, None for MAE)
- data_file: Path to your data file
- output_data: Which columns contain your output data
WHEN TO USE:
- Compare different model architectures (linear vs exponential vs polynomial)
- Select best model complexity (avoid overfitting)
- Use AIC/BIC values: lower is better
SIGMA PARAMETER:
- For MSE (Gaussian noise): Provide noise std dev from domain knowledge
- For MAE (Laplace noise): Set sigma to None
- Example: experimental measurement error ±0.1 volts → sigma=0.1
RETURNS: AIC, BIC, AICc values with interpretable model comparison metrics.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| n_obs | No | Explicit count of independent residuals. If None, infers from output data | |
| sigma | Yes | REQUIRED noise std dev for diagonal covariance Σ=σ²I. Specify from domain knowledge or estimate based on available data. | |
| data_file | Yes | Path to data file (CSV, Excel, JSON, Parquet). All data must be provided via file. | |
| loss_value | Yes | Mean loss value from optimization (MSE or MAE only) | |
| file_format | No | File format: 'csv', 'excel', 'json', 'parquet' (auto-detect if None) | |
| output_data | Yes | Output column mapping: {'columns': ['y'], 'name': 'y', 'unit': 'volt'} or {'columns': ['y1', 'y2'], 'name': 'y', 'unit': 'volt'} | |
| df_effective | No | Effective degrees of freedom for penalized models (EXCLUDING scale) | |
| n_parameters | Yes | Number of fitted parameters in mean function (scale param added automatically) | |
| n_scale_params | No | Number of scale parameters: 1 for single-output, d for d-output with separate scales | |
| aicc_include_scale | No | Include scale parameter in AICc correction (literature varies) | |
| cost_function_type | Yes | Loss function type: 'mse' (Gaussian) or 'mae' (Laplace) only | |
| include_scale_param | No | Include scale parameter (σ² or b) in k count |