Cross-validation is a process used to train a machine learning model on a subset of data to evaluate the use of the complementary subset in the model. It is an extension of the training, validation, and holdout (TVH) process and helps minimize the sampling bias that can often cause issues for machine learning models.
The goal of cross-validation is to ensure that a model will work accurately with real-world data since it is not possible to fit everything into the training data. There are three main steps in this process:
There are a few different ways to accomplish this, including the holdout method, and the K-Fold cross-validation technique.
The holdout method is the most basic form of cross-validation. It allows users to perform training with a portion of the dataset and requires them to reserve the remaining data for validation purposes. There is a higher risk for bias in this method, as the data reserved for validation may hold valuable information that should be used to train the model.
K-Fold cross-validation is a more advanced technique that involves splitting the data into k number of subsets, called folds, to perform the training on. All of the subsets are used to train the model except for one, k-1, which is used for validation. Once the value for k is selected, it is how you refer to the model – if k is 6, then it is referred to as 6-fold cross-validation.
Cross-validation is essential because even if the model appears to be highly accurate if the original data used to validate was not representative of the overall population, it will be incorrect.
This process helps businesses double-check the accuracy of a machine learning model on different subsets of data to increase your assurance that it will work on real data that it has never seen before. Cross-validation helps minimize bias and variance.
Our experts at LogicPlum understand that you need accurate models to help your business make better strategic decisions. We leverage our automated machine learning process to help you build, deploy, and maintain models within your organization while using 5-fold cross-validation to increase your confidence in the results. We also put your decision-makers in control by giving you the option to manually partition your data.
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