
Security News
Potemkin Understanding in LLMs: New Study Reveals Flaws in AI Benchmarks
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
validation-correction
Advanced tools
A package for misclassification error correction in regression using validation data
A Python package for measurement error correction in regression analysis using validation data.
pip install validation_correction
The package provides a simple interface for correcting measurement error in both linear and logistic regression using validation data. The correction is implemented using a bootstrap procedure that:
import pandas as pd
from validation_correction import validation_correction
# Load your data
research_data = pd.read_csv("research_data.csv")
validation_data = pd.read_csv("validation_data.csv")
# Run corrected regression with bootstrap
# Format: y ~ w || x + z
# where x is the true variable and w is its mismeasured version
result = validation_correction.ols(
formula="y ~ w || x + z",
data=research_data,
val_data=validation_data,
bootstrap=True, # Bootstrap is required for correction
n_boots=1000 # Number of bootstrap iterations
)
# Run naive regression (no correction)
naive_result = validation_correction.ols(
formula="y ~ w + z",
data=research_data,
val_data=None
)
# Print results with bootstrap confidence intervals
print(result)
# Plot coefficient comparison
validation_correction.plot_coefficients(naive_result, result)
# Plot bootstrap distributions
validation_correction.plot_bootstrap_distributions()
# Format: u||y ~ x + z
# where y is the true variable and u is its mismeasured version
result = validation_correction.logit(
formula="u||y ~ x + z",
data=research_data,
val_data=validation_data,
bootstrap=True
)
print(result)
The package uses a special formula syntax to specify the relationship between true and mismeasured variables:
For mismeasured predictors:
y ~ w || x + z
x
is the true variable and w
is its mismeasured versionz
) are measured without errorFor mismeasured outcomes (not yet implemented):
u || y ~ x + z
y
is the true outcome and u
is its mismeasured version~
For naive regression (no correction):
y ~ w + z
||
operator neededval_data=None
The package provides two types of visualizations:
Coefficient Comparison Plot:
validation_correction.plot_coefficients(naive_result, corrected_result)
Bootstrap Distribution Plot:
validation_correction.plot_bootstrap_distributions()
bootstrap=True
firstBootstrap confidence intervals:
result['[0.025]']
and result['[0.975]']
n_boots
parameterdata
): Must contain all variables in the formulaval_data
): Must contain both the true and mismeasured versions of the relevant variableThis project is licensed under the MIT License - see the LICENSE file for details.
FAQs
A package for misclassification error correction in regression using validation data
We found that validation-correction demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
Security News
Django has updated its security policies to reject AI-generated vulnerability reports that include fabricated or unverifiable content.
Security News
ECMAScript 2025 introduces Iterator Helpers, Set methods, JSON modules, and more in its latest spec update approved by Ecma in June 2025.