It is important to remember that an image detection model does not see an image but a set of pixel color values, or, in the case of a spam filter, a collection of characters in an email. These are raw features of the model. An important part of machine learning is feature transformation. A feature transformation we have already discussed is dimensionality reduction in regard to principle component analysis. The following is a list common feature transformations:
- Dimensionality reduction to reduce the number of features using techniques such as PCA
- Scaling or normalizing features to be within a particular numerical range
- Transforming the feature data type (for example, assigning categories to numbers)
- Adding random or generated data to augment features
Each feature is encoded on to a dimension of our input tensor, X, so in order to make a learning model as efficient...
