Machine learning notes--the similarities and differences between supervised learning and unsupervised learning

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Machine learning notes--the similarities and differences between supervised learning and unsupervised learning

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Supervised learning

In the supervised learning, there is a relationship between the input data and the output data, that is, under the given data set, the corresponding correct output results, already about know what it looks like.

Supervised learning is often categorized as a regression and classification problem.

In the regression problem, we want to get the output predicted value of the continuous value, that is, using some continuous functions to map the input values.

In the classification problem, you want to get a discrete prediction value. The input values are mapped to discrete types.

Unsupervised learning

Unsupervised learning, there is little or no perception of the end result of the problem. Extract the appropriate structure from the existing data without knowing the effects of the data variables.

Get the inner structure of the data based on the relationship of variables in the data by clustering

In unsupervised learning, there is no feedback on the predicted results. Typical example, text classification.

Reference documents:
Https://www.coursera.org/learn/machinelearning/supplement/X64SM/introduction

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Machine learning notes--the similarities and differences between supervised learning and unsupervised learning