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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