The sub-field of optimisation that specifically aims to automate model selection in machine learning. (and also occasionally ensemble construction)

There are two major approaches here that I am aware of, both of which are related in a kind of abstract way, but which are in practice different

  1. Finding the right architecture for your nueral net, a.k.a architecture search
  2. Hyperparameter optimisation which I have made into a separate notebook.

The first one I might cover here.

TODO: work out if this is the same as “meta learning”? I think not; I suspect that of being transfer learning.

Reinforcement learning approaches

Quoc Le & Barret Zoph discuss using reinforcement learning to learn neural models:

Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! […]

To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. […] in this blog post, we’ll focus on our reinforcement learning approach and the early results we’ve gotten so far.

In our approach (which we call “AutoML”), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task. That feedback is then used to inform the controller how to improve its proposals for the next round.



  • auto-sklearn, The implementation of hyperparameter optimization by Feurer et al. (2015):

    auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator:

    import autosklearn.classification
    cls = autosklearn.classification.AutoSklearnClassifier(), y_train)
    predictions = cls.predict(X_test)

    auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.


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