Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from the Industry by Yunis Ahmad Lone & Himanshu Singh

Author:Yunis Ahmad Lone & Himanshu Singh [Yunis Ahmad Lone]
Language: eng
Format: epub
Publisher: Apress
Published: 2019-11-29T16:00:00+00:00

In classification and regression problems, the data always contains a dependent variable, which is the variable that you want to predict or classify. There is one field of ML where the dependent variable is not given. These problems are the clustering or association types. Therefore, based on the data available, Machine Learning approaches fall into two areas:Supervised learning

Unsupervised learning

Supervised and Unsupervised Learning

If you are crossing a road with the support of someone, this can be called a supervised approach. But, if you start crossing the road without anyone’s help, it's an unsupervised approach. Taking a hint from this example, you can say that a supervised learning approach includes data that contains dependent variables. That’s when the model looks at the input and output variables and tries to learn the relationship between them.

With unsupervised learning, the data doesn’t contain the target variable (Y, as specified in supervised learning). It must look at the dataset and then find the similarities/differences/patterns between them. Based on that, you can have different unsupervised learning approaches. You have already seen the supervised learning approaches, which are classification and regression. Unsupervised learning approaches include clustering, decomposition, and association. Figure 4-5 represents both problem statements visually.

Figure 4-5Supervised versus unsupervised learning

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