a class named MyLinearRegression
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
Related questions
Question
Numpy
![In this homework, you're asked to write the class of Multivariate ordinary least square (OLS) regression with Numpy and test its performance with real-world
dataset. Please fill the code block cells with your code and comments, run everything (select cell in the menu, and click Run all), save the notebook, and
upload it to canvas.
I # import the packages
import numpy as np
In [1]:
Task 1: Define the class for multivariate linear regression
Define a class named MyLinearRegression for the multivariate linear regression machine learning problem. It should contain a method called fit to
estimate parameters, predict to generate predictions, and score to evaluate performance with R² value.
In Task 1, you should write your code with pure Python or Numpy, and are not allowed to use any other packages/functions in Scikit-Learn
Hints:
• For basic structures of this class, you can refer to the single-variable linear regression class defined in lecture notes because they are very similar. You
only need to replace the formulas with the mulivariate regression case.
• Please review the mathematical part of lecture notes 10 carefully before writing the code. All the formulas used here are already given in the lecture notes,
and you need to pick up the correct formulas to estimate parameters/generate predictions/evaluate performance.
• The most tricky part is about dealing with the intercepts Bo, while we have already done it for you in the fit method below.
• For linear algebra operations in Numpy, you can consult here. You can also review TA's discussion notes on Numpy.
In [ ]:
N class MyLinearRegression:
your document strings here
def fit(self, X, y):
Your document strings here](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0f342c96-a5f9-4223-96f9-f935adc6156a%2Fbeb6f6e8-1290-42da-b3ba-020f4ae66c36%2Fxn3ef8d_processed.jpeg&w=3840&q=75)
Transcribed Image Text:In this homework, you're asked to write the class of Multivariate ordinary least square (OLS) regression with Numpy and test its performance with real-world
dataset. Please fill the code block cells with your code and comments, run everything (select cell in the menu, and click Run all), save the notebook, and
upload it to canvas.
I # import the packages
import numpy as np
In [1]:
Task 1: Define the class for multivariate linear regression
Define a class named MyLinearRegression for the multivariate linear regression machine learning problem. It should contain a method called fit to
estimate parameters, predict to generate predictions, and score to evaluate performance with R² value.
In Task 1, you should write your code with pure Python or Numpy, and are not allowed to use any other packages/functions in Scikit-Learn
Hints:
• For basic structures of this class, you can refer to the single-variable linear regression class defined in lecture notes because they are very similar. You
only need to replace the formulas with the mulivariate regression case.
• Please review the mathematical part of lecture notes 10 carefully before writing the code. All the formulas used here are already given in the lecture notes,
and you need to pick up the correct formulas to estimate parameters/generate predictions/evaluate performance.
• The most tricky part is about dealing with the intercepts Bo, while we have already done it for you in the fit method below.
• For linear algebra operations in Numpy, you can consult here. You can also review TA's discussion notes on Numpy.
In [ ]:
N class MyLinearRegression:
your document strings here
def fit(self, X, y):
Your document strings here
![• For linear algebra operations in Numpy, you can consult here. You can also review TA's discussion notes on Numpy.
In [ ]:
N class MyLinearRegression:
your document strings here
def fit(self, X, y):
your document strings here
ones = np.ones((X.shape[0],1)) # column of ones
X_aug = np.concatenate((ones, X), axis =
1) # the augmented matrix, \tilde{X} in our lecture
# continue your codes
def predict(self,X):
your document strings here
# your code here
def score(self, X, y):
your document strings here
# your code here](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0f342c96-a5f9-4223-96f9-f935adc6156a%2Fbeb6f6e8-1290-42da-b3ba-020f4ae66c36%2F39t1ed_processed.jpeg&w=3840&q=75)
Transcribed Image Text:• For linear algebra operations in Numpy, you can consult here. You can also review TA's discussion notes on Numpy.
In [ ]:
N class MyLinearRegression:
your document strings here
def fit(self, X, y):
your document strings here
ones = np.ones((X.shape[0],1)) # column of ones
X_aug = np.concatenate((ones, X), axis =
1) # the augmented matrix, \tilde{X} in our lecture
# continue your codes
def predict(self,X):
your document strings here
# your code here
def score(self, X, y):
your document strings here
# your code here
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