*3.2. Data Scaling*

The values of the input features in the training and testing set were scaled (i.e., normalized) to avoid issues from different orders of magnitude among feature values. This was achieved by estimating the expected value and standard deviation of the training and testing sets and applying the normalization formula shown in Equation (2), where *zi* is the normalized data point, *xi* is the original data point, *μ* is the sample's mean or expected value, and *sd* is the sample's standard deviation. The result from this normalization procedure is a transformed dataset that presents an expected value of 0 and a standard deviation of 1.

$$z\_i = \frac{x\_i - \overline{\mu}}{sd}.\tag{2}$$
