In the lectures, we are informed that the scaling of features in a cross validation data set should use the same mean and standard error from the training set, that the predictions (yhat) should be accurate.

When reading through the code in the optional lab, it appears to me that scaling of cross validation data occurs independently. Code below:

```
# Initialize lists to save the errors, models, and feature transforms
train_mses = []
cv_mses = []
models = []
polys = []
scalers = []
# Loop over 10 times. Each adding one more degree of polynomial higher than the last.
for degree in range(1,11):
# Add polynomial features to the training set
poly = PolynomialFeatures(degree, include_bias=False)
X_train_mapped = poly.fit_transform(x_train)
polys.append(poly)
# Scale the training set
scaler_poly = StandardScaler()
X_train_mapped_scaled = scaler_poly.fit_transform(X_train_mapped)
scalers.append(scaler_poly)
# Create and train the model
model = LinearRegression()
model.fit(X_train_mapped_scaled, y_train )
models.append(model)
# Compute the training MSE
yhat = model.predict(X_train_mapped_scaled)
train_mse = mean_squared_error(y_train, yhat) / 2
train_mses.append(train_mse)
# Add polynomial features and scale the cross validation set
X_cv_mapped = poly.transform(x_cv)
X_cv_mapped_scaled = scaler_poly.transform(X_cv_mapped)
# Compute the cross validation MSE
yhat = model.predict(X_cv_mapped_scaled)
cv_mse = mean_squared_error(y_cv, yhat) / 2
cv_mses.append(cv_mse)
# Plot the results
degrees=range(1,11)
utils.plot_train_cv_mses(degrees, train_mses, cv_mses, title="degree of polynomial vs. train and CV MSEs")
```

Second from last section. I am properly misinterpreting this but cannot figure out how.