Local case-control sampling

<p>In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, local case-control sampling  is an <a href="/facts/Algorithm/fnl5NmRt">algorithm</a> used to reduce the complexity of training a <a href="/facts/Logistic_regression/mGj6mc8y">logistic regression</a> classifier. The algorithm reduces the training complexity by selecting a small subsample of the original dataset for training. It assumes the availability of a (unreliable) pilot estimation of the parameters. It then performs a single pass over the entire dataset using the pilot estimation to identify the most "surprising" samples. In practice, the pilot may come from prior knowledge or training using a subsample of the dataset. The algorithm is most effective when the underlying dataset is imbalanced. It exploits the structures of conditional imbalanced datasets more efficiently than alternative methods, such as <a href="/facts/Logistic_regression/mGj6mc8y">case control sampling</a> and weighted case control sampling.
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Local case-control sampling open-in-new

Local case-control sampling