From 83671099a6fe80dfb5abd44d1daed64ddab7484f Mon Sep 17 00:00:00 2001 From: Jaques Grobler <jaquesgrobler@gmail.com> Date: Thu, 9 Aug 2012 14:48:17 +0200 Subject: [PATCH] docstring fixes --- examples/svm/plot_svm_scale_c.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/svm/plot_svm_scale_c.py b/examples/svm/plot_svm_scale_c.py index 90c10d4ee7..5abc8089d2 100644 --- a/examples/svm/plot_svm_scale_c.py +++ b/examples/svm/plot_svm_scale_c.py @@ -27,7 +27,7 @@ increase as we add more samples. The penalization term, however, will not increase. When using, for example, :ref:`cross validation <cross_validation>`, to -set amount of regularization with `C`, there will be a different +set the amount of regularization with `C`, there will be a different amount of samples between every problem that we are using for model selection, as well as for the final problem that we want to use for training. @@ -38,7 +38,7 @@ The question that arises is `How do we optimally adjust C to account for the different training samples?` The figures below are used to illustrate the effect of scaling our -`C` to compensate for the change in the amount of samples, in the +`C` to compensate for the change in the number of samples, in the case of using an `L1` penalty, as well as the `L2` penalty. L1-penalty case @@ -46,8 +46,8 @@ L1-penalty case In the `L1` case, theory says that prediction consistency (i.e. that under given hypothesis, the estimator learned predicts as well as an model knowing the true distribution) -is not possible because of the biasof the `L1`. It does say, however, -that model consistancy, in terms of finding the right set of non-zero +is not possible because of the bias of the `L1`. It does say, however, +that model consistency, in terms of finding the right set of non-zero parameters as well as their signs, can be achieved by scaling `C1`. @@ -64,7 +64,7 @@ corresponding cross-validation scores on the `y-axis`, for several different fractions of a generated data-set. In the `L1` penalty case, the results are best when scaling our `C` with -the amount of samples, `n`, which can be seen in the first figure. +the number of samples, `n`, which can be seen in the first figure. For the `L2` penalty case, the best result comes from the case where `C` is not scaled. -- GitLab