Visualizing Sklearn Cross-validation: K-Fold, Shuffle & Split, and Time Series Split
<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's ability to predict independent data that was not used in creating it. It is also useful to cope with problems like <a href="https://en.wikipedia.org/wiki/Overfitting" rel="noopener ugc nofollow" target="_blank">overfitting</a> or <a href="https://en.wikipedia.org/wiki/Selection_bias" rel="noopener ugc nofollow" target="_blank">selection bias</a>.</p>
<p><a href="https://medium.com/towards-data-science/visualizing-sklearn-cross-validation-k-fold-shuffle-split-and-time-series-split-a13221eb5a56"><strong>Read More</strong></a></p>