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5. The Boston house price problem is a problem of predicting the housing price of each town in Boston, USA using features such as the crime rate and air pollution in the area. It can be imported from the scikit-learn package. Find the weight vector x when the Boston house price problem is solved with the linear prediction model Ax=b. Matrix and vector data can be obtained as follows. Here, to simplify the problem, we limited the input data to crime rate (CRIM), air quality (NOX), number of rooms (RM), and age (AGE), and only four data were used. Run the code below to check whether the magnitude or sign of the weight vector obtained from running the program is consistent the common notion. In order to find it, interpret the printed output for all the factors: CRIM, NOX, RM and AGE. (X Write the interpreted output like "the house price is in inverse proportion to the crime rate (CRIM).") from sklearn.datasets import load_boston

5. The Boston house price problem is a problem of predicting the housing price of each town in Boston, USA using features such as the crime rate and air pollution in the area. It can be imported from the scikit-learn package. Find the weight vector x when the Boston house price problem is solved with the linear prediction model Ax=b. Matrix and vector data can be obtained as follows. Here, to simplify the problem, we limited the input data to crime rate (CRIM), air quality (NOX), number of rooms (RM), and age (AGE), and only four data were used. Run the code below to check whether the magnitude or sign of the weight vector obtained from running the program is consistent the common notion. In order to find it, interpret the printed output for all the factors: CRIM, NOX, RM and AGE. (X Write the interpreted output like "the house price is in inverse proportion to the crime rate (CRIM).") from sklearn.datasets import load_boston

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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5. The Boston house price problem is a problem of predicting the housing price of each town in Boston, USA using features such as the crime rate and air pollution in the area. It can be imported from the scikit-learn package. Find the weight vector x when the Boston house price problem is solved with the linear prediction model Ax=b. Matrix and vector data can be obtained as follows. Here, to simplify the problem, we limited the input data to crime rate (CRIM), air quality (NOX), number of rooms (RM), and age (AGE), and only four data were used. Run the code below to check whether the magnitude or sign of the weight vector obtained from running the program is consistent the common notion. In order to find it, interpret the printed output for all the factors: CRIM, NOX, RM and AGE. (X Write the interpreted output like "the house price is in inverse prop tion to the crime rate (CRIM).") from sklearn.datasets import load boston boston=load_boston() X=boston.data y=boston.target A=X[:4, [0,4,5,6]] # 'CRIM'/NOX';RM",AGE' b=y[:4]
Transcribed Image Text:5. The Boston house price problem is a problem of predicting the housing price of each town in Boston, USA using features such as the crime rate and air pollution in the area. It can be imported from the scikit-learn package. Find the weight vector x when the Boston house price problem is solved with the linear prediction model Ax=b. Matrix and vector data can be obtained as follows. Here, to simplify the problem, we limited the input data to crime rate (CRIM), air quality (NOX), number of rooms (RM), and age (AGE), and only four data were used. Run the code below to check whether the magnitude or sign of the weight vector obtained from running the program is consistent the common notion. In order to find it, interpret the printed output for all the factors: CRIM, NOX, RM and AGE. (X Write the interpreted output like "the house price is in inverse prop tion to the crime rate (CRIM).") from sklearn.datasets import load boston boston=load_boston() X=boston.data y=boston.target A=X[:4, [0,4,5,6]] # 'CRIM'/NOX';RM",AGE' b=y[:4]
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