During Feature Engineering the task of converting categorical features into numerical is called Encoding.
There are various ways to handle categorical features like OneHotEncoding and LabelEncoding, FrequencyEncoding or replacing by categorical features by their count. In similar way we can uses MeanEncoding.
Created a DataFrame having two features named subjects and Target and we can see that here one of the features (SubjectName) is Categorical, so we have converted it into the numerical feature by applying Mean Encoding.
Code:
# importing libraries import pandas as pd # creating dataset data={'SubjectName':['s1','s2','s3','s1','s4','s3','s2','s1','s2','s4','s1'], 'Target':[1,0,1,1,1,0,0,1,1,1,0]} df = pd.DataFrame(data) print(df) |
Output:
SubjectName Target
0 s1 1
1 s2 0
2 s3 1
3 s1 1
4 s4 1
5 s3 0
6 s2 0
7 s1 1
8 s2 1
9 s4 1
10 s1 0
Code : Counting every datapoints in SubjectName
df.groupby(['SubjectName'])['Target'].count() |
Output:
subjectName s1 4 s2 3 s3 2 s4 2 Name: Target, dtype: int64
Code: groupby data with SubjectName with their mean according to their positive target value
df.groupby(['SubjectName'])['Target'].mean() |
Output:
subjectName s1 0.750000 s2 0.333333 s3 0.500000 s4 1.000000 Name: Target, dtype: float64
The output shows the mean mapped with data point in SubjectName with their positive target value (1-positive and 0-Negative).
Code : Finally assigning the mean value and map with df[‘SubjectName’]
Mean_encoded_subject = df.groupby(['SubjectName'])['Target'].mean().to_dict() df['SubjectName'] = df['SubjectName'].map(Mean_encoded_subject) print(df) |
Output : Mean Encoded Data
SubjectName Target
0 0.750000 1
1 0.333333 0
2 0.500000 1
3 0.750000 1
4 1.000000 1
5 0.500000 0
6 0.333333 0
7 0.750000 1
8 0.333333 1
9 1.000000 1
10 0.750000 0
Pros of MeanEncoding:
- Capture information within the label, therefore rendering more predictive features
- Creates a monotonic relationship between the variable and the target
Cons of MeanEncodig:
- It may cause over-fitting in the model.
