Publications by Gregg Maloy
Data 621 Blog 5
Introduction In this blog, I will demonstrate how to use the random forest model to predict the species of iris flowers in the popular ‘Iris’ dataset. Random forest is a machine learning algorithm that constructs multiple decision trees and combines their outputs to improve accuracy and reduce overfitting. Load Packages We will use the ra...
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Blog_4
Introduction In this blog, I will demonstrate how to use the hypergeometric distribution for hypothesis testing. Hypergeometric distribution is a probability distribution which describes the probability of success K after n draws without replacement from a fixed population N. Below, the probability mass function will provide the probability of ...
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Data 621 Blog 3
Introduction In this blog, I aim to explore when to use Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models. More specifically, I will outline the steps involved in determining which of these models provides the best fit for a given dataset. The data used in this blog comes from the AER package which includes a datase...
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Comparison of Ridge and Lasso Regression in Predicting Diabetes Disease Progression
Instructions This blog explores the differences between Lasso and Ridge regression in predicting diabetes disease progression. The dataset used for this analysis is commonly employed for practicing these regression techniques and is available in various packages, such as LARS. PART I: DOWNLOAD DATA & INSPECTION There are 442 rows/observations ...
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Data 621 HW 2
Instructions In this homework assignment, you will work through various classification metrics. You will be asked to create functions in R to carry out the various calculations. You will also investigate some functions in packages that will let you obtain the equivalent results. Finally, you will create graphical output that also can be used t...
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Data 605 Final
House Prices: Advanced Regression Techniques DATA 605 Final Gregg Maloy PART I: SETUP Pick one of the quantitative independent variables from the training data set (train.csv) , and define that variable as X. Make sure this variable is skewed to the right! Pick the dependent variable and define it as Y. library(readr) library(ggplot2) library(...
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Data 605 Assignment Week 15
Question 1 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. ( 5.6, 8.8 ), ( 6.3, 12.4 ), ( 7, 14.8 ), ( 7.7, 18.2 ), ( 8.4, 20.8 ). #place in df x <- c(5.6, 6.3, 7, 7.7, 8.4) y <- c(8.8, 12.4, 14.8, 18.2, 20.8) df <- data.frame(x, y) df #lm/coefficent/slope model ...
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Data 605 Discussion Week 14
In Exercises 24, write out the first 5 terms of the Binomial series with the given k-value. 24. k = 4 library(Ryacas0) x <- Sym("x") expression <- (1 + x)^4 expression_final <- Expand(expression) print(expression_final) ## yacas_expression(x^4 + 4 * x^3 + 6 * x^2 + 4 * x + 1) term 1: x^4 term 2: 4x^3 term 3: 6x^2 term 4: 4x term 5: 1...
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Data 605 Discussion Week 14
In Exercises 21, write out the first 5 terms of the Binomial series with the given k-value. 21. k = 1/2 library(Ryacas0) x <- Sym("x") expression <- (1 + x)^4 expression_final <- Expand(expression) print(expression_final) ## yacas_expression(x^4 + 4 * x^3 + 6 * x^2 + 4 * x + 1) term 1: x^4 term 2: 4x^3 term 3: 6x^2 term 4: 4x term 5: 1...
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Data 605 Assignment Week 14
Question This week, we’ll work out some Taylor Series expansions of popular functions. We’ll explore the following functions and compute their Taylor Series expansions: \(f(x) = 1/(1 - x)\) \(f(x) = e^x\) \(f(x) = \ln(1 + x)\) \(f(x) = x^{1/2}\) For each function, we will only consider its valid ranges as indicated in the notes when computin...
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