酒质量分类

Scikit-learn Pipeline的一个简单示例

受Saptashwa Bhattacharyya的 https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 启发

如何使用

from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd

REPO_ID = "julien-c/wine-quality"
FILENAME = "sklearn_model.joblib"


model = joblib.load(cached_download(
    hf_hub_url(REPO_ID, FILENAME)
))

# model is a `sklearn.pipeline.Pipeline`

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])

labels = model.predict(X[:3])
# [5, 5, 5]

model.score(X, Y)
# 0.6616635397123202

?声明

在训练此模型时没有喝红酒（遗憾）?