can you use ML algorithms to classify a non-linearly seperable dataset? 1) it is not seperable using ML algorithms 2)it can be done with high resource cost such as time and computing power 3)very rarely
Added by Eugenia O.
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A non-linearly separable dataset is one where the data points of different classes cannot be separated by a straight line (in 2D) or a hyperplane (in higher dimensions). This means that traditional linear classifiers like logistic regression or linear SVMs would Show more…
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Adi S.
a. Ensemble classifiers have been quite successful in generating supervised learning systems that exhibit very high accuracies. Can you justify this assertion using a specific example? b. If the training examples are linearly separable, how many decision boundaries can separate positive from negative data points using SVM? Which decision boundary does the SVM algorithm calculate? c. Assume that there are three correlated classification models (A, B, and C), such that the models A, B, and C have a prediction accuracy of 75%, 80%, and 65%, respectively. What is the best possible way(s) of combining these models to optimize prediction accuracy? Justify your answer.
Akash M.
Consider a classification problem where we are given a training set of n examples and labels Sn = {(x(i), y(i)) : i = 1, ..., n}, where x(i) ∈ ℐ^L and y(i) ∈ {1, -1}. Assume a different data set for the two problems below. Consider a classification problem where we are given a training set of n examples and labels Sn = {(x(i), y(i)) : i = 1, ..., n}, where x(i) ∈ ℐ^L and y(i) ∈ {1, -1}. Suppose for a moment that we are able to find a linear classifier with parameters ̑' and ̑0' such that y(i)(̑' ∙ x(i) + ̑0') > 0 for all i = 1, ..., n. Let `̑ and `̑0 be the parameters of the maximum margin linear classifier, if it exists, obtained by minimizing 1/2 ||̑||^2 subject to y(i)(̑ ∙ x(i) + ̑0) ≥ 1 for all i = 1, ..., n. Determine if each of the following statements is True or False. (As usual, "True" means always true; "False" means not always true.) 1. The minimization problem defined by the equation immediately above has a solution if and only if the training examples Sn are linearly separable. True False 2. The training examples Sn are linearly separable under our assumptions. True False 3. (̑' ∙ x(i) + ̑0') ≤ (`̑ ∙ x(i) + `̑0) for all i = 1, ..., n. True False
Ameer S.
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