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