# Metrics Report
[](https://t.me/automlalex)
[](/LICENSE)
-------------
MetricsReport is a Python package that generates classification and regression metrics report for machine learning models.
<img width=600 src="./exemples/metricsrepor_html.gif" alt="sample">
## Features
- AutoDetect the type of task
- Save report in .html and .md format
- Has several plotting functions
## Installation
You can install MetricsReport using pip:
```bash
pip install metricsreport
```
## Usage
```python
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
print(report.metrics)
# plot ROC curve
report.plot_roc_curve()
# saved MetricsReport (html) in folder: report_metrics
report.save_report()
```
More examples in the folder ./examples:
### Constructor
```python
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.
## Plots
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.
### Dependencies
* numpy
* pandas
* matplotlib
* scikit-learn
* scikit-plot
### License
This project is licensed under the MIT License.
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