Consider a simple data set shown in figure below, where we have two samples that belong...

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Consider a simple data set shown in figure below, where we have two samples that belong to class -1 and one sample from class +1. y=-1 y=+1 y=-1 We are going to use boosting algorithm to perform classification problem on the given data. We use decision stump for each weak hypothesis h. Recall that, decision stump classifier chooses a constant value c and classifies all points where x > c as one class and other points where x<c as the other class. a) 12 pts] What is the initial weight that is assigned to each data point? b) [3 pts] Show the decision boundary for the first decision stump on the figure below (indicate the positive and negative side of the decision boundary). y=-1 y=+1 y=-1 3 c) 14 pts] Circle the point whose weight increases in the boosting process. y=-1 y=+1 y=-1 1 2 4 d) [5 pts] Write down the weight that is assigned to each data point after the first iteration of boosting algorithm. Show your calculations. Consider a simple data set shown in figure below, where we have two samples that belong to class -1 and one sample from class +1. y=-1 y=+1 y=-1 We are going to use boosting algorithm to perform classification problem on the given data. We use decision stump for each weak hypothesis h. Recall that, decision stump classifier chooses a constant value c and classifies all points where x > c as one class and other points where x<c as the other class. a) 12 pts] What is the initial weight that is assigned to each data point? b) [3 pts] Show the decision boundary for the first decision stump on the figure below (indicate the positive and negative side of the decision boundary). y=-1 y=+1 y=-1 3 c) 14 pts] Circle the point whose weight increases in the boosting process. y=-1 y=+1 y=-1 1 2 4 d) [5 pts] Write down the weight that is assigned to each data point after the first iteration of boosting algorithm. Show your calculations.

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