mateuszbuda/ml-stat-util

Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers

Machine Learning Statistical Utils

Docker setup for example jupyter notebook

docker build -t stat-util .

docker run --rm -p 8889:8889 -v `pwd`:/workspace stat-util

Use cases

Code for all use cases is provided in examples.ipynb notebook.

Evaluate a model with 95% confidence interval

from sklearn.metrics import roc_auc_score

import stat_util


score, ci_lower, ci_upper, scores = stat_util.score_ci(
    y_true, y_pred, score_fun=roc_auc_score
)

Compute p-value for comparison of two models

from sklearn.metrics import roc_auc_score

import stat_util


p, z = stat_util.pvalue(y_true, y_pred1, y_pred2, score_fun=roc_auc_score)

Compute mean performance with 95% confidence interval for a set of readers

import numpy as np
from sklearn.metrics import roc_auc_score

import stat_util


mean_score, ci_lower, ci_upper, scores = stat_util.score_stat_ci(
    y_true, y_pred_readers, score_fun=roc_auc_score, stat_fun=np.mean
)

Compute p-value for comparison of one model and a set of readers

import numpy as np
from sklearn.metrics import roc_auc_score

import stat_util


p, z = stat_util.pvalue_stat(
    y_true, y_pred, y_pred_readers, score_fun=roc_auc_score, stat_fun=np.mean
)