fronni

A Python library for quickly calculating & displaying machine learning model performance metrics with confidence intervals.

How fronni works?

https://medium.com/@kaushikm/fronni-a-python-library-for-quickly-calculating-machine-learning-model-performance-metrics-with-3baf28eaa5c0

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.