1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x. Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn Note: Ensure to set the random seed to reproduce same random number during different execution times.
1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x. Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn Note: Ensure to set the random seed to reproduce same random number during different execution times.
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...
Related questions
Question
1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x. Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn
Note:
- Ensure to set the random seed to reproduce same random number during different execution times.
Transcribed Image Text:import numpy as np
mport numpy.random as rnd
np.random.seed (10)
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 3 steps with 1 images
Recommended textbooks for you
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Concepts of Database Management
Computer Engineering
ISBN:
9781337093422
Author:
Joy L. Starks, Philip J. Pratt, Mary Z. Last
Publisher:
Cengage Learning
Prelude to Programming
Computer Engineering
ISBN:
9780133750423
Author:
VENIT, Stewart
Publisher:
Pearson Education
Sc Business Data Communications and Networking, T…
Computer Engineering
ISBN:
9781119368830
Author:
FITZGERALD
Publisher:
WILEY