import neptune.new as neptune
from sklearn.datasets import load_wine
...

run = neptune.init_run()
data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(...)

PARAMS = {"n_estimators": 10, "max_depth": 3, ...}
run["parameters"] = PARAMS

clf = RandomForestClassifier(**PARAMS)
...

test_f1_score = f1_score(y_test, y_test_pred.argmax(axis=1), average="macro")
run["test_f1"] = test_f1_score
run["model"].upload("model.pkl")