From 319df2454d14215e9e1bf91f9eb4f1f2a21ace6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= Date: Mon, 29 Nov 2021 12:15:44 +1300 Subject: [PATCH] For regression, it's better to talk about targets than labels --- 05_support_vector_machines.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb index 6885af0..0a70106 100644 --- a/05_support_vector_machines.ipynb +++ b/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?_" ] }, {