SPSS Pro
一个用于统计分析和机器学习的Python库，提供简单易用的接口。
安装
pip install -e .
功能特点
- 监督学习算法
- 决策树分类
- 更多算法将陆续添加...
使用示例
决策树分类
import numpy
import pandas
from spsspro.algorithm import supervised_learning
# 生成案例数据
data_x = pandas.DataFrame({
"A": numpy.random.random(size=100),
"B": numpy.random.random(size=100)
})
data_y = pandas.Series(data=numpy.random.choice([1, 2], size=100), name="C")
# 决策树分类
result = supervised_learning.decision_tree_classifier(data_x=data_x, data_y=data_y)
# 查看结果
print(f"模型准确率: {result['accuracy']}")
print("\n特征重要性:")
print(result['feature_importance'])
依赖项
- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
1 maintainer
Metadata
1 maintainer
Release files for spsspro 0.1.1
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 | |
|---|---|---|---|---|
| spsspro-0.1.1.tar.gz | 3.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spsspro-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.6 kB
Release files / spsspro-0.1.1.tar.gz
| Download URL | spsspro-0.1.1.tar.gz |
|---|---|
| Size | 3.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c573874249cf3751e39868edf101b4a273e8b906313baf888923c076c9dedbab
|
|
BLAKE2b-256 checksum How to use checksums |
912597986de9b6c2d0ff06b290da3b1afa22d780ec3f6e15d1cc0f307d3b2222
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.3
|
Release files / spsspro-0.1.1-py3-none-any.whl
| Download URL | spsspro-0.1.1-py3-none-any.whl |
|---|---|
| Size | 4.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a616cb21ada1bcb43bf17b28b4c3a3ae95baf4eb71fdec2d76f02ecbe801c61a
|
|
BLAKE2b-256 checksum How to use checksums |
519d79cc55b59ed7569ccd62e405c97503b99e182a12cf70490669357113af96
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.3
|
Security for spsspro 0.1.1
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.
