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- Write an experiment protocol for calculating accuracy difference between touchpad pointing and mouse pointing using Laptop. You have 5 users available to perform experiment tasks. You are required to provide rationale for all your decisions. The experiment protocol should at least describe the following elements: -> Hypothesis -> Independent Variables -> Dependent Variables -> Subject Design/Group Design -> Experiment ProcedureDesign an adjacency Matrix of the alphabets of your full name. In accordance with the following conditions: Let my name is Muhammad Adnan If your name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If your Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.web design Construct a Movie class in Model architecture. Subsequently, write a method called queryMovie() to query and return all movie records that are active from movie table below
- Decision Tree / We want to learn a function that returns (counts) the number of attributes with the value 1 on a dataset, each of which takes a binary value (0 or 1) and consists of attributes named a1, a2, a3. Draw the decision tree representing this function (explain your design ideas in a few sentences).can you make ER Diagram with Crow`s foot method about library system. The rule: 1. A member can borrow more than one book at a time 2. A member can have more than one phone number 3. A book can have more than one author 4. All book registration, member and borrowing processes can only be done/served by officers 5. Data for books, members and officers are stored with a unique id, so no id is the same.Suppose you want to build a classifier for initial screening of patients who are suspected of having cancer. Patients classified as positive for cancer will be sent for further more accurate testing. The confusion matrices for classifiers A and B are given below. Which classifier is more appropriate to use to classify patients? Select one: a. Classifier A b. Classifier B c. Both are equally good and why?
- For the confusion matrix shown, what is the precision for classes A. B, and C? What is the recall for classes A, B, and C? What is the overall accuracy of the system? Normalized Confustion Matrix Classifier Output (Computed Classification) Prediction A B C True Class of the Samples (Input) A 0.81 0.08 0.11 B 0.10 0.77 0.13 C 0.11 0.15 0.74Design an adjacency Matrix of the alphabets of my name (Isha Tir Razia). In accordance with the following conditions: If name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.Instructions: In the next exercise, you will use a known dataset used as an example for classification problems. Refer to: Index of /ml/machine-learning-databases/balance-scale (uci.edu). The problem has 3 classes and 4 input features. The possible classes are (B: balanced, L: left, R: right), and the 4 input features are: (LW: left weight, RW: right weight, LD: left distance, RD: Right distance). The balance shows up the value ‘B’ when LW + LD = RD + RW. Each raw of the file data is constructed as the following: Class, LW, LD, RW, RD. Example: B, 3, 2, 4, 1. In addition, all the input features might have values 1, ..., 5. For more information refer to website given before. Remark: you are free to propose a preprocessing of the input features, also you’re advised to try different values of the learning rate and observe the training curve. Remark: in your code, you are asked to use python, numpy and matplotlib. You shouldn’t use libraries like scikit, tensorflow, pytorch, or any other…
- Load the soybean diagnosis data set in Weka (found in Weka-3.6/data/soybean.arff), then perform the following: Build a decision tree by selecting J48 as the classifier and 10-way cross-validation. Then fill out the following table: Correctly Classified Instances Incorrectly Classified Instances Kappa statistic Mean absolute error Root mean squared error Relative absolute error Root relative squared error Total Number of Instances Build a Naïve Bayes classifier and select 10-way cross-validation. Then fill out the following table: Correctly Classified Instances Incorrectly Classified Instances Kappa statistic Mean absolute error Root mean squared error Relative absolute error Root relative squared error Total Number of Instances Compare between results in previous two sections (a and b), which algorithm give the better result and…Implement a logistic regression model from scratch using Python. Assume you have a dataset containing features and binary labels (0 or 1). Your task is to write a Python class that trains a logistic regression model using gradient descent optimization.I have a 70000x785 dataset (70000 instances and 785 features) consisting of 10 different types of classes. I need to find a selection algorithm for finding the optimal training set size N as detailed in the question below. What is an example of a selection algorithm that can do this?