Difference between Supervised learning and Unsupervised learning.

Supervised learning: 


  1. Inferring a function from labeled training data.
  2. Predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction).
  3. Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting

Supervised learning examples: Predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.

Unsupervised learning: 


  1. Inferring a function to describe hidden structure of unlabeled data.
  2. We lack a response variable that can supervise our analysis.
  3. Clustering, principal component analysis, singular value decomposition; identify group of customers.

Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

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