University of Michigan - Ann Arbor

Normalization is one of the techniques used to reduce the bias and variance in statistical learning. It helps to have all the variables centered around 0 and have the same variance.
We subtract all the values of each variable from their mean and divide them by their variance.
$$\mu = \frac{1}{m} \sum_{i = 1}^{m} X^{(i)}$$
$$\sigma^{2} = \frac{1}{m} \sum_{i = 1}^{m} (X^{(i)} - \mu)^2$$
$$X_{New} = \frac{X - \mu}{\sigma}$$

Normalization of Data

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Normalization of training data may help reducing the bias and variance, and speeds up Gradient Descent, and consequently, training of the deep learning model, as shown in the figure.

Normalization Helps Deep Learning

The learning process of a decision tree involves selecting appropriate features and splitting nodes based on criteria such as information gain, the information gain ratio, or the Gini index. These splitting criteria evaluate potential split points based on the relative ordering of feature values rather than their absolute magnitudes. Consequently, applying feature scaling or data normalization to the inputs does not affect the resulting splits or the tree structure, making such preprocessing unnecessary for decision tree models.

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