Examples

import pandas as pd
from sklearn.datasets import fetch_california_housing
from synthefy_nori import NoriRegressor

X, y = fetch_california_housing(return_X_y=True, as_frame=True)
df = X.copy()
df["price"] = y

target_col = "price"
feature_cols = [c for c in df.columns if c != target_col]

train = df.sample(frac=0.8, random_state=42)
test  = df.drop(train.index)

model = NoriRegressor(model="nori-30m").fit(
    train[feature_cols].values,
    train[target_col].values,
)
test["predicted_price"] = model.predict(test[feature_cols].values)
print(test[["price", "predicted_price"]].head())

    price  predicted_price
9   2.611         2.692868
11  2.418         2.749135
13  1.913         1.884583
16  1.525         2.121786
24  1.326         1.822688