Skip to main content

evaluation of prediction of binary, multiclass and regression

Project description

# DATS6450-final-project

This package is visualization for evaluation of prediction and performances of machine learning models based on the target variables and models' prediction.
There are three types of model prediction:

* Binary classification Evaluation
* Multiclass classification Evaluation
* Regression Evaluation

## Installation

You can install `prediction_evaluation` with `pip`:

`# pip install prediction_evaluation `

## The models:

### 1. Binary classification
* Confusion matrix plot
* ROC score and AUC plot
* Precision, recall and f1-score score table

### 2. Multi-class classification
* Confusion Matrix
* ROC score and AUC plot
* Precision, recall and f1-score score table

### 3. Regression
* Mean absolute error (MAE)
* Mean Squared error (MSE)
* R^2 score
* Residual plot

<a rel="license" href=""><img alt="Creative Commons License" style="border-width:0" src="" /></a><br />This work is licensed under a <a rel="license" href="">Creative Commons Attribution-NonCommercial 4.0 International License</a>.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

prediction_evaluation-0.0.1.tar.gz (3.7 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page