| Name | train-test-sim JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Library to create simulation to find out what train test ratio is ideal |
| upload_time | 2024-04-07 08:55:37 |
| maintainer | None |
| docs_url | None |
| author | Marcel Tino |
| requires_python | None |
| license | None |
| keywords |
train test
simulation
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[English](README.md) | [Español](./docs/README.es.md) | [Français](./docs/README.fr.md) | [Deutsch](./docs/README.de.md) | [中文](./docs/README.zh.md) | [Türkçe](./docs/README.tr.md) | [日本語](./docs/README.ja.md) | [한국어](./docs/README.ko.md)
## train_test_sim
A library to create quick simulation of optimal train-test size you can keep
Developed by Marcel Tino (c) 2024
## Examples of How To Use the library
You can use this to alter according to your requirements
```
##syntax
from train_test_sim import get_simulation
model=RandomForestClassifier()
get_simulation(X,Y,model)
you can use any model on sklearn or xgboost. All you need to do is specify correct model name
```
```python
from train_test_sim import get_simulation
from sklearn.datasets import load_diabetes
import numpy as np
from sklearn.ensemble import RandomForestClassifier
diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target
# Convert the target variable to binary (1 for diabetes, 0 for no diabetes)
Y = (y > np.median(y)).astype(int)
model = RandomForestClassifier()
get_simulation(X, Y, model)
```
Note: We can create this for any model
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