testingmodel / README.md
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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 🍷