Iris Flower Classification

Model: Logistic Regression w Pipeline (StandardScaler + LogisticRegression).

Input

Table with columns:

• sepal length (cm)
• sepal width (cm)
• petal length (cm)
• petal width (cm)

Output

• predict: class (0/1/2)
• predict_proba: class probabilities

Przykład użycia

import joblib
import pandas as pd
from huggingface_hub import hf_hub_download
import numpy as np

repo_id = "studentscolab/iris"

model_path = hf_hub_download(repo_id=repo_id, filename="model.joblib")
model = joblib.load(model_path)

x = pd.DataFrame([{
    "sepal length (cm)": 5.1,
    "sepal width (cm)": 3.5,
    "petal length (cm)": 1.4,
    "petal width (cm)": 0.2,
}])

np.set_printoptions(precision=10, suppress=True)

pred = model.predict(x)[0]
proba = model.predict_proba(x)[0]
print("classes:", model.classes_)
print("pred:", pred)
print("proba:", proba)