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Manually train a hypothesis function h(x) = g(ỗ¹x) based on the following training instances using stochastic gradient ascent rule. The initial values of parameters are 0o = 0.1,0₁ = 0.1, 0₂ = 0.1. The learning rate a is 0.1. Please update each parameter at least five times. X1 0 0 1 1 X2 0 1 0 1 y 1 1 0 0

Manually train a hypothesis function h(x) = g(ỗ¹x) based on the following training instances using stochastic gradient ascent rule. The initial values of parameters are 0o = 0.1,0₁ = 0.1, 0₂ = 0.1. The learning rate a is 0.1. Please update each parameter at least five times. X1 0 0 1 1 X2 0 1 0 1 y 1 1 0 0

Database System Concepts
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
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**Training a Hypothesis Function Using Stochastic Gradient Ascent** To manually train a hypothesis function \( h_{\vec{\theta}}(\vec{x}) = g(\vec{\theta}^T \vec{x}) \), we will apply the stochastic gradient ascent rule with the given dataset. The starting values for the parameters are: - \(\theta_0 = 0.1\) - \(\theta_1 = 0.1\) - \(\theta_2 = 0.1\) The learning rate \(\alpha\) is set at 0.1. It is necessary to update each parameter at least five times. **Training Instances:** \[ \begin{array}{|c|c|c|} \hline x_1 & x_2 & y \\ \hline 0 & 0 & 1 \\ 0 & 1 & 1 \\ 1 & 0 & 0 \\ 1 & 1 & 0 \\ \hline \end{array} \] This table represents the input features \(x_1\), \(x_2\), and the target output \(y\) for training the model. Conduct the training by iterating over these instances while adjusting the parameters using the specified learning rate.
Transcribed Image Text:**Training a Hypothesis Function Using Stochastic Gradient Ascent** To manually train a hypothesis function \( h_{\vec{\theta}}(\vec{x}) = g(\vec{\theta}^T \vec{x}) \), we will apply the stochastic gradient ascent rule with the given dataset. The starting values for the parameters are: - \(\theta_0 = 0.1\) - \(\theta_1 = 0.1\) - \(\theta_2 = 0.1\) The learning rate \(\alpha\) is set at 0.1. It is necessary to update each parameter at least five times. **Training Instances:** \[ \begin{array}{|c|c|c|} \hline x_1 & x_2 & y \\ \hline 0 & 0 & 1 \\ 0 & 1 & 1 \\ 1 & 0 & 0 \\ 1 & 1 & 0 \\ \hline \end{array} \] This table represents the input features \(x_1\), \(x_2\), and the target output \(y\) for training the model. Conduct the training by iterating over these instances while adjusting the parameters using the specified learning rate.
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step: 1

 

 
Gradient Descent algorithm:-
#1. Import All Librariesimport numpy as npimport pandas as pdfrom sklearn.linear_model import Linear Regressionimport math
 
#2. Train Model Using Sklearndef
predict_using_sklean():df = pd.read_csv("test.csv")r =
Linear Regression()r.fit(df[['x1','x2']],df.y)print(r.intercept_)return r.coef_,
r.intercept_predict_using_sklean()
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