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Q3a. Given that an old farmhouse is visible on the ground and the map, recommend a way to determine the scale
of the hardcopy map.
ANSWER:-
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- Let's revisit our first problem, where we want to set up a series of chess matches so we can rank six players in our class. As we did before, we will assume that everyone keeps their chess rating a private secret; however, when two players have a chess match, the person with the higher rating wins 100% of the time. But this time, we are only interested in identifying the BEST of these six players and the WORST of these six players. (We don't care about the relative ordering or ranking of the middle four players.) Your goal is to devise a comparison-based algorithm that is guaranteed to identify the player with the highest rating and the player with the lowest rating. Because you are very strong at Algorithm Design, you know how to do this in the most efficient way. Here are five statements. A. There exists an algorithm to solve this problem using 6 matches, but there does not exist an algorithm using only 5 matches. B. There exists an algorithm to solve this problem using 7 matches,…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/37anOxiHow does the "slice and dice" method work for the multiple model?
- I need the answer as soon as possibleR You need to generate 1000 random integer values. Then create normal distribution with 100 sample of whole population, adding density to curve to distribution, draw line over the curve like bell shape, after that create qq plot and combine plot as one image.Please do not give solution in image format thanku Using the beans dataset, Build a CNN network to perform image classification using TensorFlow. Does it overfit or underfit the data? Please justify your answer. To import the Beans dataset ; import tensorflow_datasets as tfds (bn_train, bn_validation, bn_test),bn_info = tfds.load( name = 'beans', split = ['train', 'validation', 'test'], as_supervised = True, with_info = True) print(bn_info)
- Recommend a method to assess the size of the hardcopy map, given that an ancient farmhouse may be seen on the ground and on the map.Observation 1 2 3 4 5 6 Observation 7 V1 369 5 4 5 V1 5 V2 label 6 1 3 7 3 8 V2 4 Red Red Blue Blue Red Blue label ??? 6. Find the (Euclidean) distance between the 7th observation, and all the other observations 1 through 6, and then use k = 3 to determine what label observation 7 receives 7. Find the Manhatttan distance between the 7th observation, and all the other observations 1 through 6, and then use k = 3 to determine what label observation 7 receives 8. Find the Max distance between the 7th observation, and all the other observations 1 through 6, and then use k = 3 to determine what label observation 7 receives3. Generate a 5 x 5 matrix. Represent a letter like I, T, or Z. Generate the projection matrix for 0, 45, 90, and 135 degrees. Use back projection using the 4 projections to generate an estimated image.
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