What does feature extraction and selection reduce in machine learning?

Feature extraction and selection primarily focus on reducing dimensionality in machine learning. Dimensionality refers to the number of features or attributes in a dataset, and high-dimensional data can lead to challenges such as increased computational costs, the risk of overfitting, and difficulties in visualization.

By extracting and selecting only the most relevant features, models can become more efficient, as they can operate on a clearer subset of data that is more informative. This process helps to simplify the model, making it easier to interpret and often leads to improved performance by ensuring that the model learns from the most pertinent information. Reducing dimensionality also helps in making the models faster and more effective at generalizing to unseen data, as less irrelevant or redundant information is included in the training process.

While feature extraction and selection can potentially impact aspects such as noise and variance, their primary and most significant contribution is the reduction of dimensionality, which enhances the model's efficiency and effectiveness.