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AI-woordenlijst

Het complete woordenboek van kunstmatige intelligentie

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Polynomial Features

Feature engineering technique creating new variables by raising existing features to different powers to capture non-linear relationships in data.

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Polynomial Expansion

Mathematical process transforming a feature vector into a higher-dimensional space by generating all polynomial terms up to a specified degree.

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Polynomial Degree

Parameter determining the maximum power used when generating polynomial features, directly influencing model complexity.

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Polynomial Overfitting

Phenomenon where a model with high-degree polynomial features overfits training data at the expense of its generalization capability.

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Polynomial Regularization

Technique penalizing high coefficients of polynomial terms to prevent overfitting while preserving non-linear modeling capability.

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Polynomial Basis

Set of polynomial functions forming a complete system to represent any function in an extended feature space.

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Cross Features

Variables generated by multiplication or combination of existing features to reveal hidden patterns in variable interactions.

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Chebyshev Polynomials

Family of orthogonal polynomials offering better numerical stability for approximating complex functions in feature engineering.

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Polynomial Splines

Piecewise functions defined by polynomials joined continuously, offering increased flexibility to model local non-linear relationships.

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Interaction Terms

Products of original features allowing the model to capture how the effect of one variable changes according to the values of other variables.

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Linear Combination of Features

Mathematical operation creating new variables by weighted addition of existing features, often used as preprocessing before polynomial expansion.

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Curse of Dimensionality

Problem where polynomial expansion creates exponentially more features, making learning computationally expensive and requiring more data.

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Polynomial Feature Selection

Process of identifying and retaining the most relevant polynomial terms to avoid overcomplexity while maximizing the predictive power of the model.

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