Metrics

Metrics are one of the most important parts of machine learning. Unlike traditional software, in which algorithms either work or don’t work, machine learning models work in degrees. That is, there’s a continuous range of “goodness” for a model. “Metrics” are functions which measure how well a model works. There are many different choices of metrics depending on the type of model at hand.

Metric Utilities

Metric utility functions allow for some common manipulations such as switching to/from one-hot representations.

Metric Shape Handling

One of the trickiest parts of handling metrics correctly is making sure the shapes of input weights, predictions and labels and processed correctly. This is challenging in particular since DeepChem supports multitask, multiclass models which means that shapes must be handled with care to prevent errors. DeepChem maintains the following utility functions which attempt to facilitate shape handling for you.

Metric Functions

DeepChem has a variety of different metrics which are useful for measuring model performance. A number (but not all) of these metrics are directly sourced from sklearn.

Metric Class

The dc.metrics.Metric class is a wrapper around metric functions which interoperates with DeepChem dc.models.Model.