Machine learning allows software applications to predict outcomes accurately. Machine learning models leverage historical data as the input to predict output values. The performance of such models improves with time as the number of samples processed by these models increases with time.
Machine learning is of two types:
Supervised learning builds a model that makes predictions based on labelled datasets. Both the input and output datasets are defined in the model. Based on the datasets, the model makes predictions for similar input data.
The following models in CDP follow supervised learning:
Unsupervised learning is the machine learning method that extracts patterns in data. In this method, input datasets don’t have labelled responses.
Clustering is an unsupervised learning technique that finds pattern in data and also groups similar dataset to form clusters.
The following models in CDP follow unsupervised learning:
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