Feature scaling greatly affects which of the following supervised machine learning methods? 

Batch normalization is a method that helps to speed neural network training. The basic idea is that we shift and scale the inputs to every layer of neural network. 
For example, for a 3-layer neural network shown below, we want to normalize $z^{[3]}$ to speed the training of $w^{[3]}$ and $b^{[3]}$ in the third layer.

Batch Normalization in Deep Learning

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.

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

One reason that people manipulate hyperparameters is to improve the bias and variance that a given model contains. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Models with high bias pay little attention to the training data and oversimplify the model, which will lead to high errors on training and test sets. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. High Variance models do not generalize based on previous data much, if at all, and thus while it may perform well on training data it will not on test sets. Briefly, the Bias vs Variance scale is a scale between over- and under-fitting of a model. By manipulating various hyperparameters, you can change between over- and under-fitting to get the best predictions

Bias and Variance in Deep Learning

Normalization Helps Deep Learning

Why do we normalize the inputs x?

The learning process of decision tree is essentially the process of selecting appropriate features, splitting and constructing tree nodes; The criterion of splitting node is determined by the information gain, information gain ratio and Gini coefficient before and after tree construction. These indicators are not related to the size of the current eigenvalue itself.

Why not normalize the tree structure?

Bias is a learner’s tendency to consistently learn the same wrong thing.
Variance is the tendency to learn random things irrespective of the real signal. Below, Figure 1 illustrates this with an analogy of throwing darts at a board.

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