Preparing data for a network
Section of Networks in practice, part of Statistical learning
Preparing data for a network
| Type | Section |
| Taught in | Networks in practice |
| Part | Statistical learning |
Consider a table of patient attributes such as age, blood pressure, chest-pain category, and a recorded diagnosis. The example is about encoding. A numeric code must not invent a geometry that the variable does not have.
- Nominal attributes, such as chest-pain type: use separate binary input nodes, dummy variables. Coding categories 1,2,3 as a single numeric input would impose an ordering and distances between the categories that are not in the data.
- Numeric attributes: normalise to a common scale, for example mean 0 and variance 1, which also helps initialisation.
- Missing values: choose an imputation rule fitted on the training data, and apply that fitted rule to validation and test data. A training mean is a simple baseline. Zero has a meaningful interpretation only after the coding and scaling are specified. If missingness itself is informative, consider an indicator and assess the complete procedure by validation.
Used in
See also
Softmax is multinomial logistic regression · What has to be settled before a step · Weight initialisation · Variance argument for weight initialisation · Batch and online learning · Output coding, softmax, and cross-entropy · Constructing and training a network · Generalisation of a network