The long missing python library for confidence intervals

pip install confidenceinterval

This is a package that computes common machine learning metrics like F1, and returns their confidence intervals.

⭐ Very easy to use, with the standard scikit-learn naming convention and interface.

⭐ Support for many metrics, with modern confidence interval methods.

⭐ Support for both analytical computation of the confidence intervals, and bootstrapping methods.

⭐ Easy to use interface to compute confidence intervals on new metrics that don't appear here, with bootstrapping.

The motivation

A confidence interval gives you a lower and upper bound on your metric. It's affected by the sample size, and by how sensitive the metric is to changes in the data.

When making decisions based on metrics, you should prefer narrow intervals. If the interval is wide, you can't be confident that your high performing metric is not just by luck.

While confidence intervals are commonly used by statisticans, with many great R language implementations, they are astonishingly rarely used by python users, although python took over the data science world !

Part of this is because there were no simple to use python packages for this.

Getting started

from confidenceinterval import roc_auc_score
auc, ci = roc_auc_score(y_true, y_pred, confidence_level=0.95)
auc, ci = roc_auc_score(y_true, y_pred, confidence_level=0.95, method='bootstrap_bca')
auc, ci = roc_auc_score(y_true, y_pred, confidence_level=0.95, method='bootstrap_percentile', n_resamples=5000)

All methods do an analytical computation by default, but can do bootsrapping instead

By default all the methods return an analytical computation of the confidence interval (CI).

For a bootstrap computation of the CI for any of the methods belonw, just specify method='bootstrap_bca', or method='bootstrap_percentile' or method='bootstrap_basic'. These are different ways of doing the bootstrapping, but method='bootstrap_bca' is the generalibly reccomended method.

You can also pass the number of bootstrap resamples (n_resamples), and a random generator for controling the reproducability:

random_state = np.random.default_rng()
n_resamples=9999

Get a confidence interval for any external metric with Bootstrapping

With the bootstrap_ci method, you can get the CI for any metric function that gets y_true and y_pred as arguments.

As an example, lets get the CI for the balanced accuracy metric from scikit-learn.

from confidenceinterval.bootstrap import bootstrap_ci
# You can specify a random generator for reproducability, or pass None
random_generator = np.random.default_rng()
bootstrap_ci(y_true=y_true,
             y_pred=y_pred,
             metric=sklearn.metrics.balanced_accuracy_score,
             confidence_level=0.95,
             n_resamples=9999,
             method='bootstrap_bca',
             random_state=random_generator)

F1, Precision, Recall (with Macro and Micro averaging)

from confidence interval import precision_score, recall_score, f1_score

These methods also accept average='micro' or average='macro'.

The analytical computation here is using the (amazing) 2022 paper of Takahashi et al (reference below).

ROC AUC

from confidence interval import roc_auc_score

The analytical computation here is a fast implementation of the DeLong method.

Binary metrics

from confidence interval import accuracy_score,
                                ppv_score,
                                npv_score,
                                tpr_score,
                                fpr_score,
                                tnr_score
# Wilson is used by default:
ppv, ci = ppv_score(y_true, y_pred, confidence_level=0.95)
ppv, ci = ppv_score(y_true, y_pred, confidence_level=0.95, method='jeffreys')
ppv, ci = ppv_score(y_true, y_pred, confidence_level=0.95, method='agresti_coull')
ppv, ci = ppv_score(y_true, y_pred, confidence_level=0.95, method='bootstrap_bca')

For these methods, the confidence interval is estimated by treating the ratio as a binomial proportion, see the wiki page.

By default method='wilson', the wilson interval, which behaves better for smaller samples.

method can be one of ['wilson', 'normal', 'agresti_coull', 'beta', 'jeffreys', 'binom_test'], or one of the boostrap methods.

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References

The binomial confidence interval computation uses the statsmodels package: https://www.statsmodels.org/dev/generated/statsmodels.stats.proportion.proportion_confint.html

Yandex data school implementation of the fast delong method: https://github.com/yandexdataschool/roc_comparison

https://ieeexplore.ieee.org/document/6851192 X. Sun and W. Xu, "Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves," in IEEE Signal Processing Letters, vol. 21, no. 11, pp. 1389-1393, Nov. 2014, doi: 10.1109/LSP.2014.2337313.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936911/#APP2

Confidence interval for micro-averaged F1 and macro-averaged F1 scores Kanae Takahashi,1,2 Kouji Yamamoto,3 Aya Kuchiba,4,5 and Tatsuki Koyama6

B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall/CRC, Boca Raton, FL, USA (1993)

http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf Nathaniel E. Helwig, “Bootstrap Confidence Intervals”

Bootstrapping (statistics), Wikipedia, https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29