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
)