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Inference is the process of making predictions using an Estimator. You can think of an estimator inferring the outcome of a sample given the input features and the estimator's hidden state obtained during training. Once a learner has been trained it can perform inference on any number of samples.

Estimator Types#

There are 4 base estimator types to consider in Rubix ML and each type outputs a prediction specific to its type. Meta-estimators are polymorphic in the sense that they take on the type of the base estimator they wrap.

Estimator Type Prediction Data Type Example
Classifier Class label String 'cat'
Regressor Number Integer or Float 1.348957
Clusterer Cluster number Integer 6
Anomaly Detector 1 for an anomaly or 0 otherwise Integer 0

Making Predictions#

All estimators implement the Estimator interface which provides the predict() method. The predict() method takes a dataset of unknown samples and returns their predictions from the model in an array.


The inference samples must contain the same number and order of feature columns as the samples used to train the learner.

$predictions = $estimator->predict($dataset);

    [0] => cat
    [1] => dog
    [2] => frog

Estimation of Probabilities#

Sometimes, you may want to know how certain the model is about a particular outcome. Classifiers and clusterers that implement the Probabilistic interface have the proba() method that computes the joint probability estimates for each class or cluster number as shown in the example below.

$probabilities = $estimator->proba($dataset);  

    [0] => Array
            [cat] => 0.6
            [dog] => 0.4
            [frog] => 0.0
    [1] => Array
            [cat] => 0.3
            [dog] => 0.6
            [frog] => 0.1
    [2] => Array
            [cat] => 0.0
            [dog] => 0.0
            [frog] => 1.0

Anomaly Scores#

Anomaly detectors that implement the Scoring interface can output the anomaly scores assigned to the samples in a dataset. Anomaly scores are useful for attaining the degree of anomalousness for a sample relative to other samples. Higher anomaly scores equate to greater abnormality whereas low scores are typical of normal samples.

$scores = $estimator->score($dataset);

    [0] => 0.35033
    [1] => 0.40992
    [2] => 1.68153