Consider the dataset below that has 4 instances, each with two features. Notethat there is no bias or constant feature and you should not add one. Given that the weightvector is initialized to be all zeroes, what is the weight vector after the perceptron algorithmhas made one complete pass over the data in the order shown?X1 X2 Y-2 -2 1DI-22-1-12121211Will the perceptron converge on this dataset? Why or why not?

The Perceptron Algorithm is a binary classification algorithm used in Supervised Learning. It was…

Answered: Consider the dataset below that has 4…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi
Please explain What is the worse-case performance of quicksort? Select the correct answer with the correct justification. Group of answer choices a. O(n*n), because during the partitioning stage it is possible that we select a 'bad' random pivot point that only sorts one item at a time. If we continuously do this over and over again, we are effectively only sorting 1 item each time. We then need to perform this pivot step n more times to sort the remaining n-1 items, thus giving us an O(n*n) complexity. b. O(n*n), because quicksort is a comparison-based sorting algorithm in which we have to compare every possible element against every other element in order to swap each element into the correct position. c. O(n*log(n)), because each time we are selecting a pivot point, on the average-case we are able to split our problem into roughly two collections, and sort half of our elements at the pivot point.
Apply also the equal distance binning method in the sorted values The sorted values are distributed into a number of “buckets,” or bins. Price = 4, 8, 15, 21, 21, 24, 25, 28, 34, 36, 40, 50 Equal-frequency bins of size 3. Partition into (equal-frequency) bins: In smoothing by bin means, each value in a bin is replaced by the mean value of the bin. Smoothing by bin means: In smoothing by bin boundaries, each bin value is replaced by the closest boundary value. Smoothing by bin boundaries:

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