For regression, it's better to talk about targets than labels

05_support_vector_machines.ipynb

@@ -1531,7 +1531,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "_Exercise: Train and fine-tune an SVM regressor on the California housing dataset. You can use the original dataset rather than the tweaked version we used in Chapter 2. The original dataset can be fetched using `sklearn.datasets.fetch_california_housing()`. The labels represent hundreds of thousands of dollars. Since there are over 20,000 instances, SVMs can be slow, so for hyperparameter tuning you should use much less instances (e.g., 2,000), to test many more hyperparameter combinations. What is your best model's RMSE?_"
+    "_Exercise: Train and fine-tune an SVM regressor on the California housing dataset. You can use the original dataset rather than the tweaked version we used in Chapter 2. The original dataset can be fetched using `sklearn.datasets.fetch_california_housing()`. The targets represent hundreds of thousands of dollars. Since there are over 20,000 instances, SVMs can be slow, so for hyperparameter tuning you should use much less instances (e.g., 2,000), to test many more hyperparameter combinations. What is your best model's RMSE?_"
    ]
   },
   {