Managing complexity using pruning [closed]

The HR data set is licensed under a CC BY-SA 4.0 licence.

[] # Explore the size of the data set df.shape

[] # Explore types of data and feature nam df.sample()

[] # Identify the type of data in each col df.info()

[] # Explore reasons for termination df[‘EmploymentStatus’].unique()

[

] # Explore the departments dff Department’].unique()

Requiring a minimum number of samples in each leaf node dramatically prunes and simplifies a tree.

Hint: You can also explore the min_samples_split and other parameters for different pruning approaches. Refer to the enrichment activity at the end of this unit..

Import libraries

The HR data set is licensed under a CC BY-SA 4.0 licence.

[] # Explore the size of the data set df.shape

[] # Explore types of data and feature nam df.sample()

[] # Identify the type of data in each col df.info()

[] # Explore reasons for termination df[‘EmploymentStatus’].unique()

[

] # Explore the departments dff Department’].unique()