Solved Given A Data Set Xi Yi 1 Where Xi Yi E R We Chegg

1 1 10 Point Let The Data Be Xi Yi 1 Where Xi Chegg Use python to generate a synthetic data set: yi = 10 2xí ei where, xis are values evenly spaced between 1 and 10, i.e., 1 s xi s 10, and n = 50. ei is a noise follows a normal distribution with zero mean and 0.1 standard deviation. To find the least squares line that best fits the given data set { (xi, yi)}=1, we need to minimize the sum of the squared differences between the observed values yi and the values.

Solved Question Given A Data Set Xi Yi 1 Where Xi Yi E R Step 1: set up the lagrangian function. in this case, the lagrangian l is given by l (w, λ) = 1 2 * ||w||^2 λ * (y xw), where λ is the lagrange multiplier. Question: given a data set { (xi , yi)}, i = 1, , n and a prediction function ˆf, how can you measure the quality of fit? explain in the case of regression problems and classification problems. Suppose we choose to minimize the exponential loss function l (y, f) = exp ( (2y 1) f (x)), where f (x) = fo cm 1fm (x) is an additive model, fo is the intercept term and fm (x) is to be fitted by a regression tree. In this question, you will write a naive bayes classi er and verify its performance on a news group data set. as you learned in class, naive bayes is a simple classi cation algorithm that makes an assumption about conditional independence of features, but it works quite well in practice.

Question 2 Let Xi Yi T 1 Be Our Dataset With Xi Chegg Suppose we choose to minimize the exponential loss function l (y, f) = exp ( (2y 1) f (x)), where f (x) = fo cm 1fm (x) is an additive model, fo is the intercept term and fm (x) is to be fitted by a regression tree. In this question, you will write a naive bayes classi er and verify its performance on a news group data set. as you learned in class, naive bayes is a simple classi cation algorithm that makes an assumption about conditional independence of features, but it works quite well in practice. Solution: the implementation in r (see appendix) and graphs are attached. it's clear that for k = 1; 3; 5; 7 and 15, the k nearest neighbor has a smaller classi cation error for the testing dataset compared to that of the linear regression. Question: 1. given a data set { (xi,y:)}=1, where xị, yi e r, we want to find a “least squares line” û = bo b1x to fit the data set with minimum squared residuals l=1 (yi – Ûi)?. Using the gradient boosting approach, we would like to find fm 1 (x) by minimizing liai ( 9mi – fm 1 (x;))?, where imi is the functional gradient evaluated at the current step. For classification problems, we can measure the quality of fit using various metrics such as accuracy, precision, recall, f1 score, and area under the receiver operating characteristic (roc) curve.

Problem 6 Suppose You Are Given The Data Xi Chegg Solution: the implementation in r (see appendix) and graphs are attached. it's clear that for k = 1; 3; 5; 7 and 15, the k nearest neighbor has a smaller classi cation error for the testing dataset compared to that of the linear regression. Question: 1. given a data set { (xi,y:)}=1, where xị, yi e r, we want to find a “least squares line” û = bo b1x to fit the data set with minimum squared residuals l=1 (yi – Ûi)?. Using the gradient boosting approach, we would like to find fm 1 (x) by minimizing liai ( 9mi – fm 1 (x;))?, where imi is the functional gradient evaluated at the current step. For classification problems, we can measure the quality of fit using various metrics such as accuracy, precision, recall, f1 score, and area under the receiver operating characteristic (roc) curve.