Decision trees

This class of algorithms aims to predict the unknown labels splitting the dataset, by generating a set of simple rules that are learnt from the features values. For example, consider a case of deciding whether to take an umbrella today or not based on the values of humidity, wind, temperature, and pressure. This is a classification problem, and an example of the decision tree can be like what is shown in the following figure based on data of 100 days. Here is a sample table:

Humidity (%)

Pressure (mbar)

Wind (Km/h)

Temperature (C)

Umbrella

56

1,021

5

21

Yes

65

1,018

3

18

No

80

1,020

10

17

No

81

1,015

11

20

Yes

Decision tree for predicting whether to bring an umbrella or not based on a record of 100 days. ...

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