K-Fold Cross Validation
K-fold cross validation is the practice by which we separate a large data set into smaller pieces, independently process each data set, and then train our models on some number of the...
Machine Learning Bytes
Erik Partridge
Latest available episode K-Fold Cross Validation
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6 Episodes
Stratified Sampling
Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can result in...
Boosting
Boosting is also an ensemble meta-algorithm, like boosting. However, in boosting we teach a large number of weak, but specialized learners, and combine them according to their strengths. For more information on...
Bagging
Bagging is an ensemble meta-algorithm. Basically, we take some number of estimators (usually dozens-ish), train them each on some random subset of the training data. Then, we average the predictions of each...
Bias, Variance, and the Bias-Variance Tradeoff
The bias-variance trade-off is a key problem in your model search. While bias represents how well your model can capture the salient details of a problem, and generally correlates with more complex...
Empirical Risk Minimization
The concept of empirical risk minimization drives modern approaches to training many machine learning algorithms, including deep neural networks. Today's thirty second summary covers the basics of what you need to know,...
