Recursive Feature Elimination (RFE) is a feature selection technique that recursively removes the least significant features based on a model's performance until the desired number of features is reached. It is often used with machine learning algorithms to improve model accuracy and reduce overfitting by simplifying the input space.

Recursive Feature Elimination

feature selection technique

least significant features

model's performance

desired number of features

Machine learning algorithms are computational methods that enable systems to learn from data and improve their performance over time without being explicitly programmed. These algorithms are the backbone of artificial intelligence applications, driving advancements in fields such as natural language processing, computer vision, and predictive analytics.

machine learning algorithms

Model accuracy is a metric used to evaluate the performance of a predictive model by calculating the proportion of correct predictions made out of all predictions. It is crucial for determining how well a model generalizes to unseen data but should be considered alongside other metrics to avoid misleading interpretations, especially in imbalanced datasets.

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