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---
datasets:
- scikit-learn/iris
widget:
structuredData:
SepalLengthCm:
- 5.1
- 4.9
- 6.2
SepalWidthCm:
- 3.5
- 3
- 3.4
PetalLengthCm:
- 1.4
- 1.4
- 5.4
PetalWidthCm:
- 0.2
- 0.2
- 2.3
target:
- 0
- 0
- 2
tags:
- tabular-classification
---
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
REPO_ID = "d2i-pti-iu/test_svc_model"
FILENAME = "iris_svm.joblib"
model = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))
iris = load_iris()
X = iris.data[:3]
# model is a `sklearn.pipeline.Pipeline`
labels = model.predict(X)
``` |