ProgLearn provides classes and functions for biological machine learning. Notably, it improves in performance on all tasks (including past and future) with any new data. This sets it apart from classical machine learning algorithms and many other recent approaches to biological learning.
All classes and functions are available through the
ProgLearn package and can also be imported separately.
import proglearn as PL from proglearn.forest import UncertaintyForest
There are three main parts to the
ProgLearn package: Lifelong Classification Network, Lifelong Classification Forest, and the Uncertainty Forest. All three have very similar syntax and usage. A general overview is provided below with more specific and complete examples in the tutorial section. This overview example will use
proglearn.forest.UncertaintyForest but is generalizable to the Lifelong Classification Network and Lifelong Classification Forest.
First, we'll create our forest:
UF = UncertaintyForest(n_estimators = n_estimators)
Then, we fit to data:
Finally we can predict the classes of the data:
predictions = UF.predict(X_test)
Another advantage of the
ProgLearn package is the
predict_proba(X) function. It can be used to estimate the class posteriors for each example in the input data, X.
predicted_posteriors = UF.predict_proba(X_test)
This overview covers the basics of using
ProgLearn. Most use cases will utilize the functions presented above. Further examples are available in the tutorials section (see menu).