Permutation Test in Python
Here’s a Python implementation of a permutation test function for comparing means of two groups:
def permutation_test(group_a, group_b, num_permutations=10000):
observed_statistic = np.mean(group_a) - np.mean(group_b)
combined_data = np.concatenate((group_a, group_b))
permuted_statistics = []
for _ in range(num_permutations):
np.random.shuffle(combined_data)
perm_group_a = combined_data[:len(group_a)]
perm_group_b = combined_data[len(group_a):]
perm_statistic = np.mean(perm_group_a) - np.mean(perm_group_b)
permuted_statistics.append(perm_statistic)
p_value = np.sum(np.abs(permuted_statistics) >= np.abs(observed_statistic)) / num_permutations
return p_value
# Example usage:
group_a = np.array([85, 88, 90, 84, 86])
group_b = np.array([78, 80, 82, 85, 79])
p_value = permutation_test(group_a, group_b)
print("p-value:", p_value)
Output:
p-value: 0.022
This function can be used to conduct permutation tests for various types of data and test statistics in Python.
Permutation tests in Machine Learning
Permutation tests become quite useful in these situations, offering a reliable and adaptable substitute for hypothesis testing. The basic idea behind permutation testing is to generate a null distribution by randomly permuting the observed data. This allows for inference to be made without making strict assumptions about the distribution of the data. This article aims to demystify permutation tests in machine learning.
Table of Content
- What are Permutation Tests?
- Permutation Tests vs Traditional Parametric Tests
- Estimating the p-value in Permutation Tests
- P-values and its Interpretation in Permutation Tests
- Permutation Test in Python
- Benefits and Limitations of Permutation Test
- Applications of Permutation Tests in Machine Learning
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