Page 91 - Data Science Algorithms in a Week

Random Forest

We have the following variable available ['water_temperature']. As there are fewer of
them than the parameter m=3, we consider all of them. Of these, the variable with the
highest information gain is the variable water_temperature. Therefore, we will branch
the node further on this variable. We also remove this variable from the list of the available
variables for the children of the current node. For the chosen variable
water_temperature, all the remaining features have the same value: Cold. So, we end the
branch with a leaf node. We add the leaf node [swim=No].

We now add a child node [swimming_suit=None] to the node [root]. This branch
classifies two feature(s): [['None', 'Warm', 'No'], ['None', 'Warm', 'No']].

We would like to add children to the node [swimming_suit=None].

We have the following variable available ['water_temperature']. As there are fewer of
them than the parameter m=3, we consider all of them. Of these, the variable with the
highest information gain is the variable water_temperature. Therefore, we will branch
the node further on this variable. We also remove this variable from the list of the available
variables for the children of the current node. For the chosen variable
water_temperature, all the remaining features have the same value: Warm. So, we end the
branch with a leaf node. We add the leaf node [swim=No].

We now add a child node [swimming_suit=Good] to the node [root]. This branch
classifies three feature(s): [['Good', 'Cold', 'No'], ['Good', 'Cold', 'No'],
['Good', 'Cold', 'No']]
We would like to add children to the node [swimming_suit=Good].

We have the following variable available ['water_temperature']. As there are fewer of
them than the parameter m=3, we consider all of them. Of these, the variable with the
highest information gain is the variable water_temperature. Therefore, we will branch
the node further on this variable. We also remove this variable from the list of the available
variables for the children of the current node. For the chosen variable
water_temperature, all the remaining features have the same value: Cold. So, we end the
branch with a leaf node. We add the leaf node [swim=No].
Now, we have added all the children nodes for the node [root].

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