CARET TIMESLICE STEP SIZE CODE
CARET TIMESLICE STEP SIZE TRIAL

This too can be changed if you like.Ĭould you clarify what happens under the hood in the train() function when specifying a grid? I am curious about the output model from this line.Ī) Is the model that is the output from this line the BEST model with the lowest error amongst the validation sets in the cv framework? I.e. Also, each example estimates the performance of a given model (size and k parameter combination) using repeated n-fold cross validation, with 10 folds and 3 repeats. This classification dataset provides 150 observations for three species of iris flower and their petal and sepal measurements in centimeters.Įach example also assumes that we are interested in the classification accuracy as the metric we are optimizing, although this can be changed. It has two parameters to tune, the number of instances (codebooks) in the model called the size, and the number of instances to check when making predictions called k.Įach example will also use the iris flowers dataset, that comes with R. It is like k-nearest neighbors, except the database of samples is smaller and adapted based on training data. The Learning Vector Quantization (LVQ) will be used in all examples because of its simplicity. The examples in this post will demonstrate how you can use the caret R package to tune a machine learning algorithm.

CARET TIMESLICE STEP SIZE TRIAL

It will trial all combinations and locate the one combination that gives the best results. The caret R package provides a grid search where it or you can specify the parameters to try on your problem.

CARET TIMESLICE STEP SIZE CODE

Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. In this post you will discover 5 recipes that you can use to tune machine learning algorithms to find optimal parameters for your problems using the caret R package. It provides a grid search method for searching parameters, combined with various methods for estimating the performance of a given model. The caret R package was designed to make finding optimal parameters for an algorithm very easy. The best thing to do is to investigate empirically with controlled experiments. Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem.

A difficulty is that configuring an algorithm for a given problem can be a project in and of itself. Machine learning algorithms are parameterized so that they can be best adapted for a given problem.

Caret timeslice step size