@@ -42,7 +42,7 @@ roc_curve = RocCurveDisplay.from_estimator # shorthand
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We now use the `SupportVectorClassifier()` function (abbreviated `SVC()`) from `sklearn` to fit the support vector
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classifier for a given value of the parameter `C`. The
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`C` argument allows us to specify the cost of a violation to
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the margin. When the `cost` argument is small, then the margins
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the margin. When the `C` argument is small, then the margins
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will be wide and many support vectors will be on the margin or will
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violate the margin. When the `C` argument is large, then the
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margins will be narrow and there will be few support vectors on the
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