What is the expected output when applying standardization to a dataset?

When applying standardization to a dataset, the expected output is data that has a mean of 0 and a standard deviation of 1. Standardization, also known as z-score normalization, transforms the data by subtracting the mean of the dataset from each data point and then dividing by the standard deviation of the dataset. This process shifts the distribution of the data so that its center is at 0 (the mean), and it scales the spread of the data so that it stretches or compresses to have a standard deviation of 1.

This transformation is particularly useful in machine learning and statistical analyses because it ensures that features contribute equally to a model, avoiding biases that can occur due to differing scales of measurement.

In contrast, the other options describe different data transformations. A uniform scale refers to a consistent scale across different datasets or features but does not specifically indicate mean and standard deviation properties associated with standardization. Normalized values typically refer to rescaling data to a specific range, commonly [0, 1] or [-1, 1], which is a different process known as normalization. Lastly, standardization does not directly address the presence or absence of outliers; outliers can still exist in the standardized dataset, influencing the mean and standard