from huggingface_hub import hf_hub_download
import joblib
model = joblib.load(
hf_hub_download("kalifi/iris-species-classifier", "sklearn_model.joblib")
)
# only load pickle files from sources you trust
# read more about it here https://skops.readthedocs.io/en/stable/persistence.htmlIris Species Classifier
A lightweight logistic regression model trained on the classic Iris dataset. Classifies flower measurements into three species: Setosa, Versicolor, Virginica.
Usage
import mlflow.pyfunc
import pandas as pd
model = mlflow.pyfunc.load_model("kalifi/iris-species-classifier")
sample = pd.DataFrame(
[[5.1, 3.5, 1.4, 0.2]],
columns=["sepal_length", "sepal_width", "petal_length", "petal_width"]
)
print(model.predict(sample)) # ['setosa']
Performance
- Accuracy: 97.3% on held-out test set
- Training data: UCI Iris Dataset (150 samples)
- Framework: scikit-learn LogisticRegression + StandardScaler pipeline
- Downloads last month
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# Gated model: Login with a HF token with gated access permission hf auth login