Traceback (most recent call last):
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution
    executenb(
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 1204, in execute
    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 84, in wrapped
    return just_run(coro(*args, **kwargs))
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 62, in just_run
    return loop.run_until_complete(coro)
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/asyncio/base_events.py", line 642, in run_until_complete
    return future.result()
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 663, in async_execute
    await self.async_execute_cell(
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 965, in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 862, in _check_raise_for_error
    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
from sklearn import linear_model
np.random.seed(2018)
n = 10
d = 2
Lambda = 0.01

# Make data set.
x = np.linspace(-3, 3, n)
y = 2.0 + 0.5*x + 5.0*(x**2)+ np.random.randn(n)

# Design matrix X does not include the intercept. 
X = np.zeros((n, d))
for p in range(d):     
    X[:, p] = x ** (p+1)

#Split data in train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Scale data by subtracting mean value of the input using scikit-learn
scaler = StandardScaler(with_std=False)
scaler.fit(X_train)
X_train_mean = np.mean(X_train,axis=0)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
# We scale also the output, here by our own code
y_scaler = np.mean(y_train)
y_train_scaled = y_train - y_scaler
y_test_scaled = y_test- y_scaler

#Calculate beta
OLS = LinearRegression()
betaOLS=OLS.fit(X_train_scaled,y_train_scaled)
ypredictOLS = OLS.predict(X_test_scaled)
linear_model.Ridge(Lambda)
RegRidge.fit(X_train_scaled,y_train_scaled)
ypredictRidge = RegRidge.predict(X_test_scaled)
betaOLS = OLS.coef_
betaRidge = RegRidge.coef_
print(betaOLS)
print(betaRidge)
interceptOLS = np.mean(y_train) - X_train_mean @ betaOLS
interceptRidge = y_scaler - X_train_mean @ betaRidge
print(interceptOLS)
print(interceptRidge)
#predict value 
ytilde_test_Ridge = X_test_scaled @ betaRidge+y_scaler
ytilde_test_OLS = X_test_scaled @ betaOLS+y_scaler

#Calculate MSE
print("  ")
print("test MSE of OLS")
print(MSE(y_test,ytilde_test_OLS))
print("  ")
print("test MSE of Ridge")
print(MSE(y_test,ytilde_test_Ridge))
plt.scatter(x,y,label='Data')
plt.plot(x, X @ RegRidge.coef_ + RegRidge.intercept_ , label="Ridge_Fit")
plt.grid()
plt.legend()
plt.show()
------------------

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [11], in <cell line: 34>()
     32 ypredictOLS = OLS.predict(X_test_scaled)
     33 linear_model.Ridge(Lambda)
---> 34 RegRidge.fit(X_train_scaled,y_train_scaled)
     35 ypredictRidge = RegRidge.predict(X_test_scaled)
     36 betaOLS = OLS.coef_

NameError: name 'RegRidge' is not defined
NameError: name 'RegRidge' is not defined