standard_lasso

Jonathan corrected a spurious use of standard_lasso

Ch10-deeplearning-lab.Rmd

@@ -1,15 +1,3 @@
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 # Chapter 10
 
@@ -236,7 +224,7 @@ $\lambda$ with an  all-zero solution. This value equals the largest absolute inn
 X_s = scaler.fit_transform(X_train)
 n = X_s.shape[0]
 lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n
-param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), 100))
+param_grid = {'lasso__alpha': np.exp(np.linspace(0, np.log(0.01), 100))
              * lam_max}
 ```
 Note that we had to transform the data first, since the scale of the variables impacts the choice of $\lambda$.
@@ -246,7 +234,7 @@ We now perform cross-validation using this sequence of $\lambda$ values.
 cv = KFold(10,
            shuffle=True,
            random_state=1)
-grid = GridSearchCV(lasso,
+grid = GridSearchCV(standard_lasso,
                     param_grid,
                     cv=cv,
                     scoring='neg_mean_absolute_error')
@@ -868,7 +856,7 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):
     
  
 Before we start, we look at some of the training images; similar code produced
-Figure 10.5 on page  447. The example below also illustrates
+Figure 10.5 on page  406. The example below also illustrates
 that `TensorDataset` objects can be indexed with integers --- we are choosing
 random images from the training data by indexing `cifar_train`. In order to display correctly,
 we must reorder the dimensions by a call to `np.transpose()`.
@@ -1388,7 +1376,7 @@ We’ll now make a plot to compare our neural network results with the
 lasso.
 
 ```{python}
-# %%capture
+%%capture
 fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True)
 for ((X_, Y_),
      data_,