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Feature hashing is a machine learning technique that turns arbitrary features into indices in a vector or matrix by applying hash functions. It can also be used for dimensionality reduction and has geometric properties related to kernel functions.
One-hot is a technique to represent categorical data as binary vectors with only one bit set to 1. It is used in digital circuits, natural language processing, and machine learning to avoid assumptions about the order or importance of categories.
LightGBM is a free and open-source machine learning framework that uses decision tree algorithms for ranking, classification and other tasks. It is developed by Microsoft and has features such as sparse optimization, parallel training, multiple loss functions, and exclusive feature bundling.
scikit-learn is a free and open-source library for various classification, regression and clustering algorithms. It integrates with NumPy, SciPy and other Python libraries and has a long history of development and releases since 2007.
A feature is an individual measurable property or characteristic of a phenomenon used in machine learning and pattern recognition. Features can be numerical or categorical, and can be constructed, selected, or extracted to improve learning and prediction.
A dummy variable (or indicator variable) is a binary variable that indicates the presence or absence of a categorical effect in regression analysis. Learn how to use dummy variables to represent categorical variables, control for confounding factors, and avoid multicollinearity.
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Learn how to use one-class SVM (OSVM) to identify objects of a specific class from a training set containing only the objects of that class. OSVM relies on finding the smallest hypersphere that contains all the data points, and uses kernel functions to increase flexibility.