Active Machine Learning
Active Machine Learning - how does it work?
Active Learning is a type of advanced machine learning. Machine learning uses algorithms employing statistical methods to analyze a data set and find potential patterns. Inferences derived from those patterns are predictions that are tested with new data. However, unlike traditional statistics, the algorithms can perform millions of calculations, to discovery patterns of relationships in the data. Generally, machine learning methods are very robust, in that they can be applied to a variety of kinds of data, of differing quality and completeness.
Active learning is a type of advanced machine learning. It is an iterative process in which machine learning methods “learn” a predictive model using a small sample of the available data. Depending on the requirements, the machine learning methods used may include graphical models, decision trees, support vector machines, regression methods and density estimation methods.
Next, using this model, a set of recommended experiments is identified. The model chooses the experiments expected to most improve the predictive accuracy of the model. The active learning process repeats, as a new model is “learned” using the prior data and the data newly acquired from the recommended experiments. Iteratively repeating this process generates a more accurate predictive model, requiring significantly less experimentation than other predictive analytics.