6+ Kfold 使い方 Article

20160620 scikitlearn Pipeline gotchas, kfold crossvalidation from karlrosaen.com

Introduction

K-fold cross-validation is a popular technique used in machine learning for improving the accuracy of a model. It is a technique used to validate the performance of a machine learning model on an independent data set. In this article, we will be discussing the basics of K-fold cross-validation and how it is used in machine learning.

What is K-fold Cross-Validation?

K-fold cross-validation is a technique used to divide a data set into K parts, also known as folds. The data is then trained on K-1 folds and tested on the remaining fold. This process is repeated K times, with each fold being used as the test set once. This technique helps in reducing the bias in the model and gives a more generalized model.

How Does K-fold Cross-Validation Work?

The K-fold cross-validation technique follows the following steps: 1. The data set is divided into K parts or folds. 2. The model is trained on K-1 folds and tested on the remaining fold. 3. This process is repeated K times, with each fold being used as the test set once. 4. The performance of the model is then averaged over the K folds to get a more generalized estimate of the performance.

Advantages of K-fold Cross-Validation

There are several advantages of using K-fold cross-validation: 1. It helps in reducing the variance in the model. 2. It provides a more generalized estimate of the performance of the model. 3. It helps in reducing the bias in the model. 4. It is a simple technique that can be easily implemented.

Disadvantages of K-fold Cross-Validation

There are also some disadvantages to using K-fold cross-validation: 1. It can be computationally expensive, especially for large data sets. 2. It may not be suitable for some types of data sets, such as time-series data.

Conclusion

K-fold cross-validation is a powerful technique used in machine learning for improving the accuracy of a model. It is a simple but effective technique that helps in reducing the bias and variance in the model. However, it is important to consider the advantages and disadvantages of using this technique before implementing it on a data set.