Register

Go Classes | DA Course | Machine Learning

• retagged by
292 views
0 0 votes
With a non-linearly-separable dataset that contains some extra "noise" data points, using an SVM with slack variables to create a soft margin classifier, and a small value for the penalty parameter, $C$, that controls how much to penalize misclassified points, will often reduce overfitting the training data.

(Please enter 1 for True and 0 for False).

1 Answer

0 0 votes
True because small C means the penalty for mis-classifying a few points will be small and therefore we are more likely to maximize the margin between most of the points while misclassifying a few points including the noise points.
Voting & Accepting an answer helps improve the community
Answer:
← Previous · Go Classes | DA Cour...Next → · GO Classes DA Test S...
← Previous in category · Go Classes | DA Cour...Next in category → · GO Classes DA Test S...
Position:
Show:

Related questions

1 1 vote
0 0 answers
212
212 views
GO Classes asked Mar 21, 2025
212 views
Go Classes | DA Course | Machine Learning
Suppose we use leave-one-out cross validation meaning we use 7 -fold cross validation with a split of 6 to 1 between training set and validating set. Compute the average ...
0 0 votes
0 0 answers
123
123 views
GO Classes asked Mar 19, 2025
123 views
Go Classes | DA Course | Machine Learning
Which decision boundary do you prefer if little misclassifications are allowed ?(Please enter 1 for $A$ and -1 for $B$).
0 0 votes
0 0 answers
120
120 views
GO Classes asked Mar 19, 2025
120 views
Go Classes | DA Course | Machine Learning
Every trained two-class hard-margin SVM model has at least one point of each class at a distance of exactly $\frac{1}{||w||}$ (the margin width) from the decision boundar...
0 0 votes
0 0 answers
118
118 views
GO Classes asked Mar 19, 2025
118 views
Go Classes | DA Course | Machine Learning
If a finite set of training points from two classes is linearly separable, a hard-margin SVM will always find a decision boundary correctly classifying every training poi...