EzMetrics

The eaziest metrics library ever

Ez metrics is a library that calculates the fitness metrics of your ML model.

Features

• Support for fixed binary classification as well as probability based classification
• Support for regression models
• Up to 6 different metrics

And of course EzMetrics itself is open source with a public repository on GitHub.

Installation

EzMetrics requires no extra libraries to run.

pip install EzMetrics

from EzMetrics import Metrics as ezm

Usage

Create an object containing a list with the predictions of your model and a list containing the actual values por each prediction.

exmpl_obj = ezm( predicted_list, observed_list)

Then just choose a metric suited for your data and use it. In case of Mean Absolute Error it would be as follows.

exmpl_obj.mae()

Metrics

EzMetrics has 6 different metrics available, which one to use depends on your data type.

Discrete classification
Accuracy	Metrics.accuracy()
F1 score	Metrics.f1()

Probability classification
Area Under the Curve (AUC)	Metrics.roc_auc()

Regression
R squared	Metrics.r2()
Mean Absolute Error	Metrics.mae()
Mean Squared Error	Metrics.mse()

License

MIT