My Awesome CSCI 0451 Blog

CSCI 0451: Mid-Course Reflection

Introduction

When using Recursive Feature Elimination, we need to select a few variables. First, we need to select what model we want to use. Since we are using multiple linear regression, we select linear regression. We also need to select how many features we want to use, and how many we want to remove during each recursion. These we chose arbitrarily for this example to be 10 and 1 respectively.

We also chose to implement and train a second model on our data. We implemented Random Forest Regression, since it is a very powerful model for making predictions with continous data. When we train this model on our whole data set, we get a much better validation score than with linear regression. However, just like with linear regression we can use feature reduction to reduce overfitting.

Linear Regression and RFE

Notebook to practice using linear regression model and RFE function built for final project
May 14, 2023
Cece Ziegler

Predicting Bat Speed

CSCI 0451 Final Project: Create a machine learning model that predicts a players bat speed utilizing Recursive Feature Elimination, Linear Regression and Random Forest Regression models.
May 14, 2023
Cece Ziegler, David Byrne, Julia Fairbank, Sam Ehrsam

Kernel Logistic Regression

This blog post illustrates my implementation of the kernel logistic regression.
May 7, 2023
Cece Ziegler

Learning From Timnit Gebru

This blog post illustrates my implementation of singular value decomposition of an image using unsupervised learning.
Apr 16, 2023
Cece Ziegler

Unsupervised Learning

This blog post illustrates my implementation of singular value decomposition of an image using unsupervised learning.
Apr 6, 2023
Cece Ziegler

Learning From Timnit Gebru

This blog post illustrates my implementation of singular value decomposition of an image using unsupervised learning.
Apr 6, 2023
Cece Ziegler

Linear Regression

This blog post illustrates my implementation of the linear regression, using the analytical formula and gradient decent.
Mar 12, 2023
Cece Ziegler

Linear Regression

This blog post illustrates my implementation of the linear regression, using the analytical formula and gradient decent.
Mar 12, 2023
Cece Ziegler

Penguin Classification

This blog post illustrates my implementation of penguin classification.
Mar 7, 2023
Cece Ziegler

Gradient Decent

This blog post illustrates my implementation of the logistic regression, using gradient decent.
Mar 2, 2023
Cece Ziegler

Perceptron Blog

This blog post illustrates my implementation of the perceptron algorithm, along with test cases to show its performance.
Feb 22, 2023
Cece Ziegler

Hello Blog

An example blog post illustrating the key techniques you’ll need to demonstrate your learning in CSCI 0451.
Jan 10, 2023
Phil Chodrow
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