When we have a population X of data with dimension N, we are normally provided with a set (or vector) of parameters θ (for a generic parameter, we will use the notation θ) which describes some statistical characteristics of that population (namely, the mean μ). However, it is more common to deal with subsets of […]

# Tag Archives: Variance

## The Bias-Variance trade-off

Machine Learning models’ ultimate goal is making reliable predictions on new, unknown data. With this purpose in mind, we want our algorithm to capture relations in existing data and replicate them among new entries. At the same time, we do not want our algorithm to have, let’s say, prejudices because of the data it trained […]