Answer both the parts with complete details of every step Consider the following table containing the actual outputs (labels) and thecorresponding predictions of a Regression model:Actual output, yPrediction, ŷ5.27.19.58.63.62.511.713.18.49.2a. What do you understand by the term "Performance Metric"? Mention andexplain some of the performance metrices that are commonly used in case of aRegression problem.b. For the given data table, analyze the different performance metrices for theRegression model.

a. Understanding the Term "Performance Metric" In the context of machine learning and regression…

Answered: Consider the following table containing…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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1. Answer the questions:a. Draw logistic regression flow chartb. Write pseudo code of logistic regressionc. Explain each step of flow chart and pseudo coded. First design the question (choose x1 and x2 values for each class (blue and red)or initialize weight and bias),then find proper weight and bias values of greenline that can be seen in Figure 1. This line should separate two classes.
Given a dataset X consists of over one million entries of research papers published in business journals and conferences. Among these entries, there are a good number of authors that have coauthor relationships. Propose a method to efficiently mine a set of co-author relationships that are closely related (e.g. often co-authoring papers together.) What pattern evaluation measures would you apply to convincingly uncover close collaboration patterns better than others.
In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535
You’ve just finished fitting a logistic regression model for email spam detection, and it is getting abnormally bad performance on both your training and validation sets (AUC of 0.55 on Train and 0.53 on Validation dataset). You know that your implementation has no bugs, so what could be causing the problem? a. You are underfitting b. You are overfitting.
a. Feature selection can be done through both filter and wrapper method. Which of these methods is more accurate and which of these is more efficient? Explain the tradeoff and justify your answer with an example. b. Why in some situations logistic regression model is preferred over linear regression?
e) What are the methods to convert a categorical output data for using an Artificial Neural Network (ANN)? How would you use each method to convert the categorical attribute income range with possible values 10–20K, 20–30K, 30–40K, 40–50K? Which method is most appropriate? f) Explain why the updating term for the current weight in an ANN includes the negative of the sign of the derivative (slope).
a) What are Ensemble Algorithms? Write an algorithm for BOOSTING method.c) Assume that two individuals offer to sell you their predictive models M1 and M2. The confusion matrices produced by each model are as follows. What is the accuracy of each model? Assuming that precision is of paramount importance in your application, which of the two models would you buy? Why?Performance of M1Performance of M2Predicted TruePredicted FalsePredicted TruePredicted FalseActually True595Actually True8515Actually False1090Actually False955
Imagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . n Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. E:(f(x;) – t;)x; 2.E(f(x;) – t;) n 3.- E;(wx; + b – t;)x; n 4. E: (wa; +b - t;) n 4
PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?
python
1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression O K-Means Clustering K-Nearest Neighbors
Given the following methodology to train a model, explain what is wrong in the proposed methodology. What is your proposed methodology to fix the issue? We would like to develop a model to predict deformity in the hip area using the hip X-Ray. We have 640 frontal view X-ray images of 100 unique patients. The images are labeled by 3 orthopedic doctors whether they have hip deformation (as positive label) or not (as negative label). We randomly split the entire image dataset into 80% training (training and validation) and 20% testing.

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