From c649385721ef2be314776bf63f81e8b46494a9eb Mon Sep 17 00:00:00 2001 From: Sebastiaan Date: Sat, 28 Jan 2017 18:38:05 +0100 Subject: [PATCH] Change designation of the "Species" array from 'feature' to 'target array', as was likely intended. --- notebooks/05.02-Introducing-Scikit-Learn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/05.02-Introducing-Scikit-Learn.ipynb b/notebooks/05.02-Introducing-Scikit-Learn.ipynb index 951bb81bb..1ec5dabc4 100644 --- a/notebooks/05.02-Introducing-Scikit-Learn.ipynb +++ b/notebooks/05.02-Introducing-Scikit-Learn.ipynb @@ -188,7 +188,7 @@ "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n", "\n", "Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n", - "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the feature.\n", + "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n", "\n", "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:" ]