features
:
Features |
---|
{0=1.0, 1=2.0, 2=3.0} |
features
can be directly passed to ML functions.
classify
returns the predicted label by using the trained model. The trained model can not be saved natively,
and needs to be passed in the format of a nested query:
learn_libsvm_classifier
to control the
internal parameters of the model.
double
.
The following code shows the creation of the model predicting
sepal_length
from the other 3 features:
learn_libsvm_regressor
provides you a
way to control the training process.
features(double, ...)
→ map(bigint, double)learn_classifier(label, features)
→ Classifier
Returns an SVM-based classifier model, trained with the given label and
feature data sets.
learn_libsvm_classifier(label, features, params)
→ Classifier
Returns an SVM-based classifier model, trained with the given label and
feature data sets. You can control the training process by libsvm
parameters.
classify(features, model)
→ label
Returns a label predicted by the given classifier SVM model.
learn_regressor(target, features)
→ Regressor
Returns an SVM-based regressor model, trained with the given target and
feature data sets.
learn_libsvm_regressor(target, features, params)
→ Regressor
Returns an SVM-based regressor model, trained with the given target and
feature data sets. You can control the training process by libsvm
parameters.
regress(features, model)
→ target
Returns a predicted target value by the given regressor SVM model.