roncho12

1. The false omission rate of a test is 95% and its specificity is 95%.
   We perform the test on a sick subject.
   What is the probability that the test will return a positive result?

2. The sensitivity of a test is 95%.
   We also know that the test's false omission rate is 85% and that these metrics were obtained 
   when the test was evaluated on a dataset where the prevalence was 5%.
   We perform the test on a random subject, and it returns a negative result.
   What is the probability that it is a false negative?

roncho12

import numpy as np
from mlxtend.evaluate import bootstrap_point632_score


def bootstrap_estimate_and_ci(estimator, X, y, scoring_func=None, random_seed=0, 
                              method='.632', alpha=0.05, n_splits=200):
    scores = bootstrap_point632_score(estimator, X, y, scoring_func=scoring_func, 
                                      n_splits=n_splits, random_seed=random_seed, 
                                      method=method)
    estimate = np.mean(scores)

roncho12

import numpy as np
from mlxtend.evaluate import bootstrap_point632_score
from sklearn.metrics import recall_score
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression

# Create a dataset
X, y = make_classification(n_samples=1000, n_features=5, n_redundant=0)
# Add some noise to the features
X += np.random.normal(0, 2.5, X.shape)