## Nested Cross-Validation

### An Introduction, Overview, and scikit-learn Example

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]

## Cross Validation #2

### scikit-learn's KFold, StratifiedKFold, LeaveOneOut, GroupKFold, and TimeSeriesSplit Classes

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]

## Cross-Validation #1

### Validation Sets, Leave-One-Out Cross-Validation, and k-Fold Cross-Validation

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]

## Quadratic Discriminant Analysis

### An Introduction, the Bias-Variance Trade-Off, and a Comparison to Linear Discriminant Analysis Using scikit-learn

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]

## Linear Discriminant Analysis #2

### scikit-learn, Precision, Recall, F-scores, ROC Curves, and a comparison to Logistic Regression

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]