1. Finetune Learning
2. Finetuned Meaning

tidymodels, finetune, tune

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Max Kuhn

We’re thrilled to announce the first release of the finetune package. finetune adds two additional approaches for model tuning.

You can install it from CRAN with:

This blog post will describe the two new tools in the package.

Racing

Tuning parameters are unknown quantities of a model that cannot be directly estimated from the data. The number of neighbors in a K nearest neighbor model is a good example.

Grid search is a common method to find good values for model tuning parameters. A pre-defined set of parameters are created and often resampled so that good estimates of model performance are available. The user then choses a tuning parameter value that has acceptable results.

The problem with this approach is that it requires all of the results to be able to make a decision. For example, if we evaluate 50 tuning parameter values on 10 resamples, 500 model fits are evaluated before any analysis of the results takes place.

Racing methods, devised by Maron and Moore (1994), enables a sequential type of grid search. All model parameters are evaluated on a few resamples. Racing methods analyze the initial results to determine if any of the tuning parameters are unacceptable enough to discard. If the analysis discards any parameters, they are not resampled further. This process can considerably reduce the total number of model evaluations. finetune has functions tune_race_anova() and tune_race_win_loss() for this purpose (with syntax similar to tune_grid()). Their analysis details are described in Kuhn (2014).

As an example, we’ll tune a K nearest neighbor (KNN) model on the sonar data. The grid will consist of 20 tuning parameters values in conjunction with 25 bootstrap resamples.

Using this approach, we evaluated 156 models (less than half of the full set of 500). The help file describes how parallel processing can also be used to further speed up the racing process.

There is also a handy plotting function that demonstrates how models are eliminated in the racing process:

Each line corresponds to a tuning parameter combination. Models with suboptimal ROC scores are eliminated quickly.

Simulated Annealing

Simulated annealing is an old global search method that does a controlled random walk. In our application, the walk is through the tuning parameter space. finetune uses it as an iterative search method where new tuning parameter values are created during the process (as opposed to a pre-defined grid). The tune_sim_anneal() function has syntax that is very similar to the tune_bayes() function.

For our KNN model:

We’ll have more information on both these methods in a next release of chapters for Tidy Modeling with R.

ILMerge is a utility that can be used to merge multiple .NET assemblies into a single assembly. ILMerge takes a set of input assemblies and merges them into one target assembly. The first assembly in the list of input assemblies is the primary assembly. When the primary assembly is an executable, then the target assembly is created as an executable with the same entry point as the primary assembly. Also, if the primary assembly has a strong name, and a .snk file is provided, then the target assembly is re-signed with the specified key so that it also has a strong name.
ILMerge is packaged as a console application. But all of its functionality is also available programmatically.
There are several options that control the behavior of ILMerge. See the documentation that comes with the tool for details.

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Finetune Learning

Finetuned Meaning