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
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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Question
![**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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb6d57dbf-1951-461b-ba86-1f9bc3e1f7b9%2F1758508e-e043-439e-8f7b-b6b58de92f67%2Ffo8ma7_processed.jpeg&w=3840&q=75)
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.
Expert Solution
Step 1
: Solution ::
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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