Difference between Regression vs Classification vs Clustering

・In Japanese
＜premise knowledge＞
・Machine learning
・Regression , Classification , Clustering(k-means)

Regression, classification, and clustering are all data processing techniques used in machine learning. Explain each difference.

■Difference between Regression and Classification

Regression and classification express the relationship between objective and explanatory variables from existing data, and both are similar in that they make predictions for unknown data. In regression, the prediction result is the numerical information of the data, while in classification, the prediction result is the attribute information of the data. as below.

■Difference between classification and clustering

Classification assumes that the attributes of existing data are known in advance, and predicts where the attributes of the data will be classified when unknown data comes from there. This is called "supervised learning". Clustering, on the other hand, is a method of grouping by finding common features from the premise that the attributes of the data are unknown. This is called "unsupervised learning". Note that naming the grouped attributes is done by humans, but attributes do not necessarily have to have names.

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