Machine Learning (ML) is a class of pattern finding methods that lies at the intersection of statistics and Artificial Intelligence (AI) and, algorithm-wise, represents the state-of-the-art of predictive analytics. ML methods include Artificial Neural Networks (ANN), Decision Trees (DT), Clustering algorithms and Support Vector Machines (SVM) to name a few.
These methods always (when done correctly) generate better predictions, typically 10-30% better, than the base-line MLR (Multiple Linear Regression) simple statistics method.
These Machine Learning methods work by fitting far more complex non-linear relationships between your predictor variables and your target prediction output. So, for example, ANN (artificial neural networks) fit your historical data to your desired prediction by adjusting the strength of thousands of connections (synapses) in neuron-like algorithmic structures during ‘training’ on your historical data. Once trained, the ANN ML system can predict future events and will have very similar accuracy to that gained during training if verified during training by a test subset of data.
The upshot is that even non-linear relationships can be modeled so your prediction is more accurate than the base-line MLR method which is linear by nature.
The take home message is that, machine learning, when done correctly, can enable you to use the full information content in your customer data to it’s 100% potential. Although one can argue about which ML method is the best, once you settle on the best one for your data you can be confident that there is almost nothing else you could do to get more bottom-line information out of your data than by using ML.
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