Inspect machine learning models
Project description
Model Inspector
Inspect machine learning models
Install
pip install model_inspector
How to Use
Example:
from IPython.display import HTML
import sklearn.datasets
from sklearn.linear_model import LinearRegression
from model_inspector.sklearn import generate_linear_model_html
diabetes = sklearn.datasets.load_diabetes()
X, y = diabetes["data"], diabetes["target"]
HTML(
generate_linear_model_html(
model=LinearRegression().fit(X, y),
feature_names=diabetes["feature_names"],
target_name="progression",
)
)
progression = 152.13
<span style='color:green'>- 10.01</span>
* <span style='color:blue'>age</span>
<span style='color:green'>- 239.82</span>
* <span style='color:blue'>sex</span>
<span style='color:green'>+ 519.84</span>
* <span style='color:blue'>bmi</span>
<span style='color:green'>+ 324.39</span>
* <span style='color:blue'>bp</span>
<span style='color:green'>- 792.18</span>
* <span style='color:blue'>s1</span>
<span style='color:green'>+ 476.75</span>
* <span style='color:blue'>s2</span>
<span style='color:green'>+ 101.04</span>
* <span style='color:blue'>s3</span>
<span style='color:green'>+ 177.06</span>
* <span style='color:blue'>s4</span>
<span style='color:green'>+ 751.28</span>
* <span style='color:blue'>s5</span>
<span style='color:green'>+ 67.63</span>
* <span style='color:blue'>s6</span>
The library also supports logistic regression with model_inspector.sklearn.generate_logistic_model_html
.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
model_inspector-0.0.5.tar.gz
(9.9 kB
view hashes)
Built Distribution
Close
Hashes for model_inspector-0.0.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 592fe018fd9b119d2f396f78cc32884015206be370004185598a0d5661d5e690 |
|
MD5 | 45c9cbbd930da3407104d4221fc5b75b |
|
BLAKE2b-256 | 417f92c1672d9a0b3a8eec99f6c547b503dd1b55005ad73df8da8bc161acec63 |