Visualizing 3 Sklearn Cross-validation: K-Fold, Shuffle & Split, and Time Series Split

<h2>What is Cross-validation?</h2> <p>Basically, cross-validation is a statistical method for evaluating learning algorithms. A fixed number of folds (groups of data) is set to run the analysis. These folds group the data into 2 sets: training and testing (validation) sets, that are cross-over in rounds, allowing each data point to be validated.</p> <p>The main purpose is to test the model&#39;s ability to predict independent data that was not used in creating it. It is also useful to cope with problems like&nbsp;<a href="https://en.wikipedia.org/wiki/Overfitting" rel="noopener ugc nofollow" target="_blank">overfitting</a>&nbsp;or&nbsp;<a href="https://en.wikipedia.org/wiki/Selection_bias" rel="noopener ugc nofollow" target="_blank">selection bias</a>.</p> <p><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*pRe5vFBSKvTcaH0bk9Fp8A.png" style="height:391px; width:700px" /></p> <p>An example of results from cross-validations in this article. Image by Author.</p> <p>In this article, we are going to apply Python to visualize the process of 3 cross-validation types from the&nbsp;<a href="https://scikit-learn.org/stable/modules/cross_validation.html" rel="noopener ugc nofollow" target="_blank">Scikit Learn</a>&nbsp;library:</p> <ul> <li>K-Fold cross-validation</li> <li>Shuffle &amp; Split cross-validation</li> <li>Time Series Split cross-validation</li> </ul> <p>Moreover, the validation results can also be plotted to express insightful information.</p> <p><a href="https://towardsdatascience.com/visualizing-sklearn-cross-validation-k-fold-shuffle-split-and-time-series-split-a13221eb5a56"><strong>Visit Now</strong></a></p>