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Generating reports on metrics for Machine Learning models

Project description

Metrics Report

PyPI - Python Version PyPI Telegram License

MetricsReport is a Python package that generates classification and regression metrics report for machine learning models.



  • AutoDetect the type of task
  • Save report in .html and .md format
  • Has several plotting functions


You can install MetricsReport using pip:

pip install metricsreport


from metricsreport import MetricsReport  

# sample classification data 
y_true = [1, 0, 0, 1, 0, 1, 0, 1] 
y_pred = [0.8, 0.3, 0.1, 0.9, 0.4, 0.7, 0.2, 0.6]  

# generate report 
report = MetricsReport(y_true, y_pred, threshold=0.5)  

# print all metrics 

# plot ROC curve 

# saved MetricsReport (html) in folder: report_metrics

More examples in the folder ./examples:


MetricsReport(y_true, y_pred, threshold: float = 0.5)
  • y_true : list
    • A list of true target values.
  • y_pred : list
    • A list of predicted target values.
  • threshold : float
    • Threshold for generating binary classification metrics. Default is 0.5.


following methods can be used to generate plots:

  • plot_roc_curve(): Generates a ROC curve plot.
  • plot_all_count_metrics(): Generates a count metrics plot.
  • plot_precision_recall_curve(): Generates a precision-recall curve plot.
  • plot_confusion_matrix(): Generates a confusion matrix plot.
  • plot_class_distribution(): Generates a class distribution plot.
  • plot_class_hist(): Generates a class histogram plot.
  • plot_calibration_curve(): Generates a calibration curve plot.
  • plot_lift_curve(): Generates a lift curve plot.
  • plot_cumulative_gain(): Generates a cumulative gain curve plot.


  • numpy
  • pandas
  • matplotlib
  • scikit-learn
  • scikit-plot


This project is licensed under the MIT License.

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