Machine Learning: Classification

Machine Learning: Classification

Example:  E-mail classification algorithm can learn to predict whether a given email is spam or ham (no spam). 

Machine Learning Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data.

In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data.

There are four main classification tasks: binary, multi-class, multi-label, and imbalanced classifications.  Multi-class classification is most used one.

Inventory ABC is an example of multi-classes.  One can use the training data and supervise the machine learning to learn the classification.  Upon validation, the model can be used to predict which class a given input part belong to. 

In supply chain management, SKUs (Stock Keeping units) are organized into different classes, upon which various strategies and policies are applied.  Classification can be utilized to manage inventory classes 

Classification is one classical machine learning. There are well developed software tools and methods.  We will discuss it in Machine Learning Tools section.

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