What is Machine Learning

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Supervised Learning

The main difference between supervised and unsupervised machine learning is that supervised learning uses labeled data. Labeled Data is a data that contains both the Features (X variables) and the Target (y variable).

When using supervised learning, the algorithm iteratively learns to predict the target variable given the features and modifies for the proper response in order to “learn” from the training dataset. This process is referred to as Training or Fitting. Supervised learning models typically produce more accurate results than unsupervised learning but they do require human interaction at the outset in order to correctly identify the data. If the labels in the dataset are not correctly identified, supervised algorithms will learn the wrong details.

Examples