Guided Ordinal Encoding Techniques

There are specifically two types of guided encoding techniques for categorical features, namely – target guided ordinal encoding & mean guided ordinal encoding.

Tools and Technologies needed:

1. Understanding of pandas library
2. Basic knowledge of how a pandas Dataframe work.
3. Jupyter Notebook or Google Collab or any similar platform.

What is encoding?

 Encoding is the technique we use to convert categorical entry in a dataset to a numerical data. Let say we have a dataset of employees in which there is a column that contains the information about the city location of an employee. Now we want to use this data to form a model which could predict the salary of an employee based upon his/her other details. Obviously, this model doesn’t understand anything about the city name. So how will you make the model know about it? For example, an employee who lives in a metropolitan city earns more than employees of a small city. Someway we need to make the model know about this . Yes, the way you are thinking in your mind is what we will do through code. As obvious we are thinking to rank the city based upon some spec . These ways of converting a categorical data to a numerical data are our target. 

What is target guided encoding technique?

In this technique we will take help of our target variable to encode the categorical data . lets understand by an example,

Employee Id	City	Highest Qualification	Salary
A100	delhi	Phd	50000
A101	delhi	bsc	30000
A102	mumbai	msc	45000
B101	pune	bsc	25000
B102	kolkata	phd	48000
C100	pune	msc	30000
D103	kolkata	msc	44000

Lets try to encode the city column using the target guided encoding. Here our target variable is salary.

step 1: sort the cities based upon the corresponding salary. Now to do this we will take mean of all the salaries of that particular city.

step 2: Based upon the mean of the salary  the descending order of the city is :

                                                         kolkata>mumbai>delhi>pune

step3: Based upon this order we will rank the cities.

City	Rank
kolkata	4
mumbai	3
delhi	2
pune	1

(note: you can rank them in the opposite order too)

step 4 : we will use this information to encode the City column of the dataset.

Employee Id	City	Highest Qualification	Salary
A100	2	phd	50000
A101	2	bsc	30000
A102	3	msc	45000
B101	1	bsc	25000
B102	4	phd	48000
C100	1	msc	30000
D103	4	msc	44000

This is all what target guided encoding is! simple right? Lets now explore about mean guided encoding.

What is mean guided encoding technique?

We will encode the Highest qualification column using the mean guided encoding technique.

step 1: For each highest qualification we will find the mean of all the corresponding salary.

step 2 : Instead of ranking them based upon the mean value , we will encode this mean value corresponding to the respective highest qualification

Highest Qualification	Mean Salary
Phd	49000
Msc	39666.67
Bsc	27500

step 3 : We will use this to encode the Highest Qualification column

Employee Id	City	Highest Qualification	Salary
A100	2	49000	50000
A101	2	27500	30000
A102	3	39666.67	45000
B101	1	27500	25000
B102	4	49000	48000
C100	1	39666.67	30000
D103	4	39666.67	44000

Hence we are ready with our dataset to prepare our model.

My Personal Notes arrow_drop_up

Save