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The following is the cost function of linear regression in machine learning. When learning using the...
1. Choose all the valid answers to the description about linear regression and logistic regressio...
machine learning/ stats questions
1. Choose all the valid answers to the description about linear regression and logistic regression from the options below: A. Linear regression is an unsupervised learning problem; logistic regression is a super- vised learning problem. B. Linear regression deals with the prediction of co ontinuous values; logistic regression deals with the prediction of class labe C. We cannot use gradient descent to solve linear regression: we must resort to least square estimation to compute a closed-form...
linear regression 1.1 Localized Suppose we want to estimate localized linear regression by weighting the contribution...
linear regression 1.1 Localized Suppose we want to estimate localized linear regression by weighting the contribution of the data points by their distance to the query pointr,i.e. using the cost i=1 where (r)-2(is the inverse Euclidean distance between the training point and query (test) point Derive the modified normal equations for the above cost function E(r(a)). (Hint: first, rewrite the cost function in matrix/vector notation, using a diagonal matrix to represent the weights w).
The first one is from the pre set of notes. The second one is during class....
The first one is from the pre set of notes. The second one is
during class. They are both saying the same (I think).
Simple Linear Regression: You are given the following training data. X Y 2.05.1 2.56.1 3.06.9 3.57.8 4.09.2 4.59.9 5.0 11.5 5.5 12.0 6.0 12.8 a. Model Y as a linear function of X. b. Use gradient descent learning algorithm to learn model parameters. Use an appropriate learning rate and convergence criterion. Plot J for the learning...
Which of the following are assumptions for the linear regression model? CHECK THAT ALL MAY APPLY!!! Select one or more: a. Regression function (i.e., equation) is linear. b. Error terms are normall...
Which of the following are assumptions for the linear regression model? CHECK THAT ALL MAY APPLY!!! Select one or more: a. Regression function (i.e., equation) is linear. b. Error terms are normally distributed. c. Error terms are independent. d. Error terms have constant variance. e. Regression model fits all observations (i.e., no outliers).
A statistical model is developed by training the machine learning algorithm using training data. In most...
A statistical model is developed by training the machine learning algorithm using training data. In most cases, this is just a subset of all the possible data for the problem for which the model is being developed. We want to develop a model that also works well with unseen data, called test data. The models that we build can overfit or underfit the data. With this in mind, which of the following statements is false: Using Ridge regression versus linear...
Using the Instructor ranking data conduct a simple linear regression to predict a student’s scores based...
Using the Instructor ranking data conduct a simple linear regression to predict a student’s scores based on the number of hours the student studies and answer the following questions: What is the value of the intercept of this regression equation? What is the value of the slope of this regression equation and what is its interpretation within the context of this problem? Write the equation for this logistic regression model R2 = What does this number mean? F= Explain how F (F...
In a simple linear regression, the following information is given
In a simple linear regression, the following information is given: \(\bar{x}=-25 ; \bar{y}=56 ; \sum\left(x_{i}-\bar{x}\right)\left(y_{i}-\right.\) \(\bar{y})=1250 ; \sum\left(x_{i}-\bar{x}\right)^{2}=711\)a. Calculate \(b_{1}\)b. Calculate \(b_{0}\)c. What is the sample regression equation? Predict \(y\) if \(x\) equals \(-20\).
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation Error Training Error Error Error Error Training set size Training set size Model-1 Training set size Model-3 Model-2 A) Model-1 B) Model-2 C) Model-3 Q55) [1 Point] Maximal margin classifiers are sensitive to outliers in training data. A) True B) False Q56) [1 Point] Soft margin classifiers allows for misclassification in training data. A) True B) False Q57) [1 Point] Which of the following...
Question 2: A multiple linear regression analysis is performed and the following MINITAB output is observed:...
Question 2: A multiple linear regression analysis is performed and the following MINITAB output is observed: Regression Analysis: Fuel cell power versus H2 pressure and H2 flow The Regression Equation is Fuel cell power (W) = 2705.235 - 1.0745*H2 pressure (psi) + 3.7707*Hz flow (stoc) Term Coef SE Coef T-Value Constant 334.44 H2 pressure (psi) 9.09 Ha flow (stoc) 2.18 MS F-Value Analysis of Variance Source DF Regression Error Total 27 SS 3770.7 7751.78 Answer to the following questions based...
machine learning/ stats questions
1. Choose all the valid answers to the description about linear regression and logistic regression from the options below: A. Linear regression is an unsupervised learning problem; logistic regression is a super- vised learning problem. B. Linear regression deals with the prediction of co ontinuous values; logistic regression deals with the prediction of class labe C. We cannot use gradient descent to solve linear regression: we must resort to least square estimation to compute a closed-form...
linear regression 1.1 Localized Suppose we want to estimate localized linear regression by weighting the contribution of the data points by their distance to the query pointr,i.e. using the cost i=1 where (r)-2(is the inverse Euclidean distance between the training point and query (test) point Derive the modified normal equations for the above cost function E(r(a)). (Hint: first, rewrite the cost function in matrix/vector notation, using a diagonal matrix to represent the weights w).
The first one is from the pre set of notes. The second one is
during class. They are both saying the same (I think).
Simple Linear Regression: You are given the following training data. X Y 2.05.1 2.56.1 3.06.9 3.57.8 4.09.2 4.59.9 5.0 11.5 5.5 12.0 6.0 12.8 a. Model Y as a linear function of X. b. Use gradient descent learning algorithm to learn model parameters. Use an appropriate learning rate and convergence criterion. Plot J for the learning...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation Error Training Error Error Error Error Training set size Training set size Model-1 Training set size Model-3 Model-2 A) Model-1 B) Model-2 C) Model-3 Q55) [1 Point] Maximal margin classifiers are sensitive to outliers in training data. A) True B) False Q56) [1 Point] Soft margin classifiers allows for misclassification in training data. A) True B) False Q57) [1 Point] Which of the following...
Question 2: A multiple linear regression analysis is performed and the following MINITAB output is observed: Regression Analysis: Fuel cell power versus H2 pressure and H2 flow The Regression Equation is Fuel cell power (W) = 2705.235 - 1.0745*H2 pressure (psi) + 3.7707*Hz flow (stoc) Term Coef SE Coef T-Value Constant 334.44 H2 pressure (psi) 9.09 Ha flow (stoc) 2.18 MS F-Value Analysis of Variance Source DF Regression Error Total 27 SS 3770.7 7751.78 Answer to the following questions based...
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