R Shiny Code – Pima Indians Diabetes Data Set

• April 16, 2018
• Pascal Schmidt

The Shiny App that was created with the code below can be found here and in our previous blog post. Read More

R Shiny App – Model Assumption Checking of Quadratic and Linear Discriminant Analysis

• April 9, 2018
• Pascal Schmidt

In this blog post, we are going to check the assumptions of linear and quadratic discriminant analysis with a shiny App . The data we used for the app can be found here and the code can be found here. We have already gone through the assumptions in this R tutorial.   Read More

Classification – Quadratic vs. Linear Discriminant Analysis (Pima Indians Data Set)

• April 2, 2018
• Pascal Schmidt

In previous blog posts, we have discussed the theory behind the linear and quadratic discriminant analysis and we have also examined the assumptions for the Pima Indians Data Set with a Shiny App. In this blog post, we are going to implement these two algorithms and see which one performs better. First, we are going to load all the required… Read More

How important is Parsimony versus Accuracy?

• March 26, 2018
• Pascal Schmidt

There are two main approaches in Statistics when it comes to model building. One is to come up with a model that is simple to interpret and explains the relationship between and well. The other one is to build a model that yields accurate predictions regardless of the form of and the complexity of the model. In a perfect world,… Read More

Assumption Checking of LDA vs. QDA – R Tutorial (Pima Indians Data Set)

• February 19, 2018
• Pascal Schmidt

In this blog post, we will be discussing how to check the assumptions behind linear and quadratic discriminant analysis for the Pima Indians data. We also built a Shiny app for this purpose. The code is available here. Let’s start with the assumption checking of LDA vs. QDA. What we will be covering: Data checking and data cleaning Checking assumption of… Read More

Linear vs. Quadratic Discriminant Analysis – Comparison of Algorithms

• February 12, 2018
• Pascal Schmidt

In this blog post, we will be looking at the differences between Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Both statistical learning methods are used for classifying observations to a class or category. So that means that our response variable is categorical. Let us get started with the linear vs. quadratic discriminant analysis tutorial. What we will be… Read More

Online or In Class? Stanford’s Statistical Learning Online vs. University

• February 5, 2018
• Pascal Schmidt

After having taken the Statistical Learning course from Stanford and also a university course that was about the same material I am going to review and compare both courses. Let’s jump into the analysis of Stanford’s statistical learning online vs. university. What we are going to cover: Some Background Lectures vs. Online Videos Tutorial vs. Video Labs Comparing the Testing… Read More

Classification Versus Regression in Machine Learning

• January 29, 2018
• Pascal Schmidt

When dealing with a data set, the first thing you want to determine is whether you are dealing with a regression problem or a classification problem and then choose the most appropriate model to your problem. Let’s jump into the classification versus regression tutorial. What we are going to cover: Classification Binary Classification Multiclass Classification Algorithms for Classification Choosing a… Read More