From 946b6a3ea33f4a380498d72f9c772c1341745750 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 19 Jan 2024 16:47:21 +0100 Subject: [PATCH] FIX fix some typos (#759) --- .../linear_models_feature_engineering_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python_scripts/linear_models_feature_engineering_classification.py b/python_scripts/linear_models_feature_engineering_classification.py index 12a2997da..153afea21 100644 --- a/python_scripts/linear_models_feature_engineering_classification.py +++ b/python_scripts/linear_models_feature_engineering_classification.py @@ -350,10 +350,10 @@ def plot_decision_boundary(model, title=None): # %% [markdown] # -# The polynomial kernel approach would be interesting in cases were the +# The polynomial kernel approach would be interesting in cases where the # original feature space is already of high dimension: in these cases, # **computing the complete polynomial expansion** with `PolynomialFeatures` -# could be **intractable**, while Nyström method can control the output +# could be **intractable**, while the Nyström method can control the output # dimensionality with the `n_components` parameter. # # Let's now explore the use of a radial basis function (RBF) kernel: