fronni
A Python library for quickly calculating & displaying machine learning model performance metrics with confidence intervals.
How fronni works?
Requirements
-
Python >= 3.6
-
numba
-
numpy
-
scikit-learn
-
plotly
Installing fronni
pip install fronni
Full documentation
Functions from the classification module:
classification_report
Generates confidence intervals for precision, recall, & F1 metrics for a binary or multi-class classification model, given arrays of predicted & label values.
| Parameter | Type | Default |
|---|---|---|
| label | Numpy array or Pandas series | None |
| predicted | Numpy array or Pandas series | None |
| n | integer, number of bootstrap iterations | 1,000 |
| confidence_level | integer value between 1 & 100 | 95 |
| as_dict | Boolean, return nested dictionary if True otherwise Pandas dataframe | False |
| confidence_level | value between 1 & 100 | 95 |
| sort_by_sample_size | Boolean, return the Pandas dataframe, sorted in descending order of class sample size | False |
plot_classification_report
Plots precision, recall, & confidence intervals for F1 metrics for a binary or multi-class classification model, given a classification report input.
| Parameter | Type | Default |
|---|---|---|
| report | output from classification_report | None |
| save_to_filename | string, path of filename image to save like "image.png" | None |
From the regression module:
regression_report
Generates confidence intervals for RMSE, MAE, and R^2 metrics for a regression model, given arrays of predicted & label values.
| Parameter | Type | Default |
|---|---|---|
| label | Numpy array or Pandas series | None |
| predicted | Numpy array or Pandas series | None |
| n | integer, number of bootstrap iterations | 1,000 |
| as_dict | Boolean, return nested dictionary if True otherwise Pandas dataframe | False |
See the CONTRIBUTING file for how to help out.
License
fronni is Apache 2.0 licensed, as found in the LICENSE file.
Metadata
Release files for fronni 0.0.6
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Release files / fronni-0.0.6.tar.gz
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