The inputs to machine learning algorithms are called features. Features can include mathematical transformations of data elements that are relevant to the machine learning task, for example, the total value of financial transactions in the last week, or the minimum transaction value over the last month, or the 12- week moving average of an account balance.
Supervised techniques require a set of inputs (features) and corresponding outputs to “learn from” in order to build a predictive model. Supervised learning algorithms learn by tuning a set of model parameters that operate on the model’s inputs, and that best fit the set of outputs. The goal of supervised machine learning is to train a model of the form y = f(x), to predict outputs, y based on inputs, x.
After features, x, are designed and implemented and observations, y, are identified, the model y = f(x) is ready to be trained. During model training, the machine learning algorithm “learns” parameters or weights, which are applied to the features to generate a trained model to best fit the outputs. Examples of model parameters include coefficients in a linear regression or split points in a decision tree.
C3 AI software provides extensive capabilities that enable data scientists, developers, and analysts to identify, understand, and work with features across large data sets, in order to design and deploy robust machine learning models.