This is the second post in a short series discussing the common regularization methods of ridge regression and the lasso. In an earlier post, I introduced much of the theory surrounding these methods. For a more detailed overview of regularization, please see that earlier post. [Read More]

Regularization is a method of fitting a model containing all predictors $p$ that regularize the coefficient estimates towards zero. Also known as constraining or shrinking the model’s coefficient estimates, regularization can significantly reduce the model’s variance and thus improve test error estimates and model performance. The two most commonly used... [Read More]

The bootstrap is a widely used resampling technique first introduced by Bradley Efron in 1979 commonly used to quantify the uncertainty associated with a given estimator or statistical learning method. The bootstrap can be applied to many problems and methods, and is commonly used to estimate the standard errors of... [Read More]

Nested cross-validation can be viewed as an extension of simpler cross-validation techniques. When performing model selection or model evaluation, $k$-fold cross-validation is a crucial method for estimating a particular model’s test error on unseen observations. However, as Cawley and Talbot discussed in a 2010 paper, when performing model selection and... [Read More]

In a previous post I introduced the concept of cross-validation as a resampling technique. In particular, cross-validation is useful for estimating the test error of a particular model fit in order to evaluate its performance, or to decide on an optimal level of flexibility. In addition, cross-validation can also be... [Read More]

Resampling methods are a crucial tool used commonly in modern statistics and data science. These methods involve taking repeated samples from a training dataset and refitting a model of interest on each individual sample to obtain additional information about the fitted model. These methods allow us to learn new information... [Read More]

In this post, I’ll be exploring quadratic discriminant analysis. I’ll compare and contrast this method with linear discriminant analysis, and work through an example using scikit-learn and the slimmed down Titanic dataset from one of my prior posts on logistic regression. [Read More]

This post is the second in a series on linear discriminant analysis (LDA) for classification. In the first post, I introduced much of the theory behind linear discriminant analysis. In this post, I’ll explore the method using scikit-learn. I’ll also discuss classification metrics such as precision and recall, and compare... [Read More]

This post is the first in a series on the linear discriminant analysis method. In this series, I’ll discuss the underlying theory of linear discriminant analysis, as well as applications in Python. [Read More]

I recently read through this excellent Medium article about the ColumnTransformer estimator in scikit-learn and how it can be used in tandem with Pipelines and the OneHotEncoder estimator. To strengthen my own understanding of the concept, I decided to follow the post with my own working example, and summarize the... [Read More]