Get desktop application:
View/edit binary Protocol Buffers messages
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Generate a large imbricated if-else structure. This solution is slow, consumes a lot of memory, and is mostly implemented for experimentation.
Compute the prediction with the routing algorithm. This solution has the best overall complexity, size and speed (though some other methods might sometime be faster).
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Output of a classification model.
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The model returns an integer representing the predicted class. This option is fast and simple to use.
For a binary classification model, the prediction output is an accumulator of type "accumulator_precision" where the sign (positive or negative) indicates the predicted label value. For a multi-class classification model, the predicted output is an array of accumulators where the largest value defines the predicted label. This option is fast but hard to use.
For a binary classification model, the prediction output is the probability of the positive class. For a multi-class classification model, the predicted output is an array of probabilities for all the classes. Note: This probability might be not calibrated if the model is not calibrated. This option is easy to use but slow.
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High level options to control the model embedding.
Name filename and namespace of the generated library.
What is the Predict function is returning in case of a classification model.
If true, integerize the output computation. The model will be smaller and faster, but the predictions will be less accurate.
Precision of the accumulator when integerize_output=true;
Algorithm used to compute the tree output.
Add a monitor of the library used in the generated code.
If true, generates functions to create categorical feature values from strings. For example, for a categorical feature "X" with an associated "FeatureX" enum class, the method "FeatureXFromString(absl::string_view name) -> FeatureX" is created. Enabling this option might increase the binary size.
Which programming language should be produced.