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Opening the Black Box of Machine Learning

Demand forecasting is a vital aspect of planning out your business model—being able to anticipate market trends and responses enables you to respond in turn. To better perform demand forecasting, many businesses turn to machine learning software to analyze data and extrapolate from it. For many others, though, one question stands out: what exactly is machine learning?

machine learning software

How Does a Machine Learn?

Machine learning refers to the use of various computational methods and algorithms to analyze data, determine patterns, construct models to fit the underlying rules and extrapolate from other data sets using these models. This takes advantage of computers’ capacity for rapid calculations and number crunching to handle large data sets that would be tedious, if not impossible, for humans. Many algorithms exist that each approach this process in different ways, but they can be grouped into “supervised” or “unsupervised” learning. The former builds a model from corresponding input and output datasets to describe how they’re correlated and predict other outputs; the latter works with unlabeled datasets to find hidden trends, group similar data points or highlight outliers.

The ML model is built and refined in an ongoing, multi-stage process. Following the training dataset, a second dataset is used to validate the model and determine how well it performs. The results of the validation set are used to adjust hyperparameters—parameters that are set before machine learning begins to influence how the model develops—then the model is applied to a real-world test set. It’s vital that the data used for training and testing is representative of real-world data—experienced data scientists need to assemble these datasets, as well as anticipate and account for bias in the resulting models. In some cases, the model may be subject to further testing in order to improve it, or a new model may be generated if the current one is found to have too much error.

Machine Learning and Demand Forecasting

The task of demand forecasting entails massive amounts of data that must be used to predict behavior of a complex system—what drives demand, how likely these drivers are to change, what the results of these changes would be, etc. This makes machine learning software a potentially valuable tool. The ability to collate all of this data and interpret it with high accuracy, then present it in a meaningful way, is critical to making good forecasting decisions. Especially valuable traits of ML software include being able to automatically take in new data, as well as data from many sources along the supply chain, and use it to continuously adjust the model.

Business owners and investors need more data with more accuracy as the matter of supply and demand becomes more complex. Advances in machine learning are what make it possible to succeed in an ever-changing market.

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