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Machine learning models train on examples taken from the real-world environment they represent.
Machine learning models were built in R using the caret package [56].
Machine learning models are created using the Weka machine learning toolkit.
Atolla use two distinct machine learning models.
However, traditional unit tests — the backbone of traditional software testing — don't really work with machine learning models, because the correct output of machine learning models isn't known beforehand.
These data are to train the machine learning models to predict leak source locations.
That is, traditional machine learning models — not deep neural networks — are powering most AI applications.
Like most machine learning models, this one also became better as it gathered more data.
For example, in 2015, it also outsourced FeatureFu, a toolkit for building machine learning models.
Comet.ml allows data scientists and developers to easily monitor, compare and optimize their machine learning models.
Developers don't think of machine learning models as Android- or iOS-specific, after all.
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