---
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="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.
Metadata
Release files for prediction-evaluation 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| prediction_evaluation-0.0.1.tar.gz | 3.7 kB | Without provenance | Details for prediction_evaluation-0.0.1.tar.gz |
Release files / prediction_evaluation-0.0.1.tar.gz
| Download URL | prediction_evaluation-0.0.1.tar.gz |
|---|---|
| Size | 3.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c2ae6541b52454cdcb26eb7d54d2849ec9a984bbda48fdd06ecab02443f2201f
|
|
BLAKE2b-256 checksum How to use checksums |
d86705a01322c1370a1075919d57e52adc3da5bbc5d1dc29646c084a8216ce38
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/38.4.0 requests-toolbelt/0.8.0 tqdm/4.23.0 CPython/3.6.4
|
Security for prediction-evaluation 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.