Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model. Any dataset would be good. Just want to understand it. Please use the same format below that I am using so I can follow along with your explanation. CODE GIVEN from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21,21,1000) y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1)
Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model. Any dataset would be good. Just want to understand it. Please use the same format below that I am using so I can follow along with your explanation. CODE GIVEN from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21,21,1000) y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1)
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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Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model. Any dataset would be good. Just want to understand it. Please use the same format below that I am using so I can follow along with your explanation.
CODE GIVEN
from mpl_toolkits import mplot3d
from matplotlib.ticker import LinearLocator, FormatStrFormatter
%matplotlib inline
%matplotlib notebook
x_fit = np.linspace(-21,21,1000)
y_fit = x_fit
x_fit = x_fit.reshape(-1,1)
y_fit = y_fit.reshape(-1,1)
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