Training Models

One of the most basic operations in machine learning is supervised learning, where a model learns from labeled data so it can later be used to predict results when it encounters similar data. The predictions can be classifications which produce a discrete category, or a regression where the model prediction is of a continuous response.

Examples of classification problems are:

• A diagnosis or fraud detection, producing "true" or "false"
• An image recognizer, producing "cat", "dog", "car" or "airplane"

Regression problems do things like:

• Predicting a price
• Forecasting production quantities

The technique for training a model is similar regardless of the desired output.

The next two tutorials illustrate training on two distinctly different kinds of data. The first uses tabular data from multiple financial institutions to train an overdraft prediction model. The second tutorial trains on unseen image data from another organization to create a neural network capable of recognizing handwritten digits.