18 Questions
Which vector is normal to the hyperplane in the given context?
3
What would be the consequence if all 'positive' points were misclassified as 'negative' and vice versa?
Only the 'negative' points would be classified correctly
What makes finding the linear classifier with the least training error a challenging task?
Computational complexity
Which approach is suggested to consider before attempting clever solutions to a problem?
Generating random parameter vectors
How many hypotheses are typically generated in the discussed simple learning algorithm?
K
What is the primary objective in learning linear classifiers with regards to training error?
Minimize training error
What is the primary assumption made about the training data and testing data?
They are drawn independently from the same probability distribution
What is the purpose of evaluating a student's ability to generalize in a problem set analogy?
To evaluate their ability to generalize to new data
What is the training error of a classifier h?
$\frac{1}{n} \sum_{i=1}^{n} I(h(x^{(i)}) \neq y^{(i)})$
What is a hypothesis class H in the context of machine learning?
A set of possible classifiers, each representing a mapping from Rd → {−1, +1}
What is the primary goal when finding a classifier with small training error?
To generalize well to new data and have a small test error
What is the output of a learning algorithm?
An element h of H
What is the primary purpose of evaluating the performance of a learning algorithm?
To determine the test error on a learned hypothesis h
What is a potential source of variability in computing test error on a learned hypothesis h?
The particular training examples in Dn and randomization inside the algorithm
Why is it necessary to execute the process of training and testing multiple times?
To control for possible poor choices of training set or unfortunate randomization inside the algorithm
What is a concern when evaluating the performance of a learning algorithm?
Having too little data to do multiple iterations of training and testing
What is the purpose of randomly sampling θ(j) and θ0 in the RANDOM-LINEAR-CLASSIFIER algorithm?
To find the best hypothesis in the hypothesis class
What is the best method to evaluate the performance of a classifier h?
Measuring test error on data not used to train it
Explore the concept of training and testing data being drawn independently from the same probability distribution. Learn about defining training error for a classifier and aiming for a small training error that generalizes well to new data.
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