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1. Which machine learning algorithms do not need to be normalized?
In machine learning, most algorithms benefit from data normalization, as normalization can help the algorithm converge faster and improve the performance of the model. However, not all algorithms strictly require normalization, the following are some cases where normalization is not necessary:
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Decision Trees and Random Forests: Tree models such as decision trees and random forests are generally not affected by the scale of features, since the split at each node is based on a threshold of a feature. However, normalization can still improve training speed and model stability.
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Naive Bayes: The Naive Bayes algorithm is insensitive to scale variations of features because it is based on the assumption of conditional independence of features. However, if the distribution of features is different, normalization can still improve the performance of the algorithm.
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Clustering algorithm: In some clustering algorithms, such as K-Means, the scale of the data can affect the clustering results. While normalization is common practice for algorithms such as K-Means, in some cases it is possible to obtain reasonable clustering results without normalization.
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Decision Rule Algorithms: Some decision rule-based algorithms (such as association rule mining) do not involve numerical calculation of features, but make judgments based on the presence or absence of features, so scale does not affect the results.
Although these algorithms may not require normalization in some cases, in general, data normalization can improve the performance, stability and convergence speed of the model. Therefore, in practical applications, unless there is a special need, it is usually recommended to normalize the data.