Cross-validation is a statistical method used to estimate the performance of a model on unseen data. It is widely used for model validation in both classification and regression problems. In this post, we will explore how to perform cross-validation for regression models in R using packages such as caret and glmnet.

Stepwise regression is a popular method used for selecting a subset of predictor variables by either adding or removing them from the model based on certain criteria. In this blog post, we will learn how to perform stepwise regression in R using the Bayesian Information Criterion (BIC) as the selection criterion.

Calculus is at the core of many scientific, engineering, and statistical problems. Fortunately, R, a powerful programming language for data analysis and computation, can also solve calculus problems like limits, derivatives, and integrals. In this post, we'll explore how to tackle these problems using R.

One of R’s great strengths is its ability to simulate random processes and perform various types of sampling. Whether you're running Monte Carlo simulations, bootstrapping, or just generating random numbers, R provides powerful tools for these tasks. In this blog post, we'll dive into simulating random processes and how to perform sampling in R.

Functions are an essential part of programming in R, allowing you to create reusable code that can streamline workflows, simplify complex operations, and make your scripts more organized. In this blog post, we’ll walk through how to write functions in R, covering everything from the basic syntax to more advanced topics like using default arguments and return values.

Neural networks provide a powerful tool for predictive modeling, capable of capturing complex relationships in data. In this blog post, we will explore how to implement a neural network using the nnet package in R. We will build a neural network model to predict gender, education, and age based on personality test data. Furthermore, we will compare the neural network's performance to traditional regression models using Root Mean Squared Error (RMSE).

Predictive modeling is a core technique in data science, and using machine learning frameworks can greatly improve both the accuracy and speed of model development. In this blog post, we explore how to use the h2o package in R to automate the model building process with H2O's AutoML, and compare it with traditional regression models. We'll also calculate performance metrics using the Root Mean Squared Error (RMSE).

When working with large datasets, computational efficiency becomes critical. In this post, we will explore different methods of parallel processing in R to improve execution time, leveraging the parallel, foreach, and future packages. We'll also compare sequential and parallel strategies for linear modeling and matrix operations.

I recently learned that Google requires affiliate links to be “nofollow” links. Compared to the often default “follow” links, “nofollow” links essentially remove the website endorsement typically implied by websites linking to each other.

I go over methods for data imputation for training machine learning models. These techniques are inappropriate for hypothesis testing because they do not account for the uncertainty in the imputed data. However, if you are training a neural network on thousands of rows of data, and have missingness, these methods could be a good solution.

Much of what we do in statistics requires that the data we are using be normally distributed. This prolific assumption requires that we either visually inspect the data or use a hypothesis test. While hypothesis tests like the Shapiro-Wilk test offer a clear-cut decision, it is sometimes preferred to simply visually inspect the data.

A guide to performing many different types of t-tests including: one-sample, two-sample assuming equal variance, two-sample assuming unequal variance, and two-sample dependent measures.

I became interested in making functions that describe data being fed through a pipe stream without changing it.

Naive Bayes is a computationally simple, but incredibly effective method for classification. In this tutorial, I will show you how to run this model and determine the classification accuracy of the model.

In this tutorial, you will learn how to create a random forest classification model and how to assess its performance.

We may encounter situations where we can not store the data set and calculations required for regression models in RAM. This article presents a technique where we can estimate regression coefficients by sampling from a data set and running smaller regression calculations.

One of the most powerful aspects of R is that it has a diverse set of random number generators. We can use these R tools to create methods of obscuring a message in what appears to be meaningless strings of text (cryptography).

A while statement will run as long as the conditional statement it is given evaluates as true. Naturally, the basic setup then is to make a conditional statement that will change over time and eventually evaluate as false when it no longer needs to keep running.

One of the most initially confusing aspects of R is using loops to repeat a chunk of code. I hope you will find this tutorial helpful and start to understand the power loops give to our code.

There will be instances where you will want to create a vector that contains a repeated value or special sequence.