[QUESTION] Chapter 4, how do I roll back model parameters? (Early Stopping)

Hi,

Please be patient, I am very new to machine learning. The Early Stopping section introduces an implementation. I ran the code and everything went smooth. My question is how I use the epoch and model that I just found. Here's the code:

```python

from copy import deepcopy

#Creating the data
np.random.seed(42)
m = 100
X = 6 * np.random.rand(m, 1) - 3
y = 2 + X + 0.5 * X**2 + np.random.randn(m, 1)

X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)

# Preparing the data
poly_scaler = Pipeline([
("poly_features", PolynomialFeatures(degree=90, include_bias=False)),
("std_scaler", StandardScaler())
])
X_train_poly_scaled = poly_scaler.fit_transform(X_train)
X_val_poly_scaled = poly_scaler.transform(X_val)

sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True, penalty=None, learning_rate="constant", eta0=0.0005)

minimum_val_error = float("inf")
best_epoch = None
best_model = None
for epoch in range(1000):
sgd_reg.fit(X_train_poly_scaled, y_train) #this continues where it left off
y_val_predict = sgd_reg.predict(X_val_poly_scaled)
val_error = mean_squared_error(y_val, y_val_predict)
if val_error < minimum_val_error:
minimum_val_error = val_error
best_epoch = epoch
best_model = deepcopy(sgd_reg)

```

**Versions**
- OS: Windows 10.0.19044 Build 19044
- Python: 3.9.7
- Scikit-Learn: 0.24.2

The best epoch is around 230 ish. I just don't understand how I can use best_model to make predictions on X. Thank you so much in advance.