Confidence Intervals
for the AI Era

Evaluate your AI models with statistical confidence intervals using robust bootstrap resampling methods. Fast, accurate, and easy to use.

Or install locally via pip

$pip install infer-ci
View on PyPI v0.2.1

See the Uncertainty.
Not just a point estimate.

Infer generates detailed visualizations of your model's performance distribution. Identify stability issues, compare models with statistical rigor, and communicate results effectively.

Confidence Interval
Bootstrap Samples

Bootstrap Distribution

Uncertainty Estimation

95% LCB 95% UCB Mean: 0.850.750.800.850.900.95

Multiple Metrics

Support for extensive regression (MAE, MSE, RMSE, R²) and classification metrics (Accuracy, F1, AUC) out of the box.

Bootstrap Resampling

Compute statistical confidence intervals using robust bootstrap resampling techniques for reliable uncertainty estimation.

Visual Analysis

Interactive visualizations showing bootstrap distribution and confidence intervals for better model performance understanding.

Simple Input Format.
Values that matter.

Just upload a CSV file with two columns: y_true and y_pred. Infer handles the statistical heavy lifting for you.

  • Automatic metric detection
  • Instant confidence calculation
  • Exportable results
input.csv
# Required CSV columns
y_true,y_pred
1,0.92
0,0.15
1,0.88
0,0.05
1,0.96
...

Ready to evaluate your models?

Join thousands of data scientists who trust Infer for their statistical evaluations and confidence interval calculations.