# Saving and Loading Models — NumPy / Pandas / scikit-learn

Source: https://www.skillbyai.com/en/numpy-pandas-sklearn/x-save

> joblib and versions.

## Persist the whole pipeline

Save the fitted **pipeline** (not just the model) with `joblib.dump`, together with metadata: scikit-learn version, training date, data snapshot and metrics. Load it with the **same library versions**: pickled models are not guaranteed to work across scikit-learn versions. Only load model files from trusted sources, because unpickling can execute code; for cross-language serving, consider ONNX or skops.

## Saving, reloading and inspecting a forest, run

I ran this with Python 3.12.3 and scikit-learn 1.9.1 on a dataset bundled with scikit-learn (no download). Predictions are identical after reload, the version is stored with the model, and feature importances show petal measurements dominate.

```python
import os, tempfile
import joblib, sklearn
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

X, y = load_iris(return_X_y=True)
model = RandomForestClassifier(n_estimators=50, random_state=0).fit(X, y)
path = os.path.join(tempfile.mkdtemp(), "iris_rf.joblib")
joblib.dump({"model": model, "sklearn_version": sklearn.__version__}, path)

bundle = joblib.load(path)
same = (bundle["model"].predict(X) == model.predict(X)).all()
print("predictions identical after reload:", same)
print("saved with scikit-learn", bundle["sklearn_version"])
for name, imp in zip(load_iris().feature_names, model.feature_importances_):
    print(f"{name:18} {imp:.3f}")
```

Output:

```
predictions identical after reload: True
saved with scikit-learn 1.9.1
sepal length (cm)  0.083
sepal width (cm)   0.027
petal length (cm)  0.442
petal width (cm)   0.448
```

## Pin versions in requirements

Record exact numpy, pandas and scikit-learn versions next to every saved model.

**Quiz:** Why should you never load a pickled model from an untrusted source?

- [ ] It deletes training data
- [ ] It is always too large
- [x] Unpickling can execute arbitrary code
- [ ] It changes the model's accuracy

*Answer:* Unpickling can execute arbitrary code. Pickle is not a safe format for untrusted input.