metadata
tags:
- tabular-classification
- sklearn
datasets:
- wine-quality
- lvwerra/red-wine
widget:
structuredData:
fixed_acidity:
- 7.4
- 7.8
- 10.3
volatile_acidity:
- 0.7
- 0.88
- 0.32
citric_acid:
- 0
- 0
- 0.45
residual_sugar:
- 1.9
- 2.6
- 6.4
chlorides:
- 0.076
- 0.098
- 0.073
free_sulfur_dioxide:
- 11
- 25
- 5
total_sulfur_dioxide:
- 34
- 67
- 13
density:
- 0.9978
- 0.9968
- 0.9976
pH:
- 3.51
- 3.2
- 3.23
sulphates:
- 0.56
- 0.68
- 0.82
alcohol:
- 9.4
- 9.8
- 12.6
Wine Quality classification clone for testing
A Simple Example of Scikit-learn Pipeline
Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya
How to use
from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd
REPO_ID = "wlaminack/testingmodel"
FILENAME = "sklearn_model.joblib"
model = joblib.load(cached_download(
hf_hub_url(REPO_ID, FILENAME)
))
# model is a `sklearn.pipeline.Pipeline`
Get sample data from this repo
data_file = cached_download(
hf_hub_url(REPO_ID, "winequality-red.csv")
)
winedf = pd.read_csv(data_file, sep=";")
X = winedf.drop(["quality"], axis=1)
Y = winedf["quality"]
print(X[:3])
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.7 | 0 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 |
| 1 | 7.8 | 0.88 | 0 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.2 | 0.68 | 9.8 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.997 | 3.26 | 0.65 | 9.8 |
Get your prediction
labels = model.predict(X[:3])
# [5, 5, 5]
Eval
model.score(X, Y)
# 0.6616635397123202
🍷 Disclaimer
No red wine was drunk (unfortunately) while training this model 🍷