## Train, Test, And Validation Sets

Learn why it is best practice to split your data into training, testing, and validation sets, and explore the utility of each with a live machine learning model.

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Learn why it is best practice to split your data into training, testing, and validation sets, and explore the utility of each with a live machine learning model.

When it comes to evaluating classification models, accuracy is often a poor metric. This article covers two common alternatives, Precision and Recall, as well as the F1-score and Confusion Matrices.

Learn how the majority vote and well-placed randomness can extend the decision tree model to one of machine learning's most widely-used algorithms, the Random Forest.

Explore one of machine learning's most popular supervised algorithms: the Decision Tree. Learn how the tree makes its splits, the concepts of Entropy and Information Gain, and why going too deep is problematic.

Understand the tradeoff between under- and over-fitting models, how it relates to bias and variance, and explore interactive examples related to LOESS and KNN.

Meet the double descent phenomenon in modern machine learning: what it is, how it relates to the bias-variance tradeoff, the importance of the interpolation regime, and a theory of what lies behind.

Deepen your understanding of the double descent phenomenon. The article builds on the cubic spline example introduced in Double Descent 1, describing in mathematical detail what is happening.