LabelEncoding and Ordinal Encoding of Ordinal Categorical Features| Feature Encoding Tutorial 2
During the Feature Encoding in Machine Learning Training pipeline we encode the categorical features into numbers.
We can generally divide the categorical variables(features) into 3 types:
1. Binary:
(Yes, No) , (True, False)
2. Ordinal: Specific ordered Groups.
economic status (“low income”,”middle income”,”high income”),
education level (“high school”,”BS”,”MS”,”PhD”),
income level (“less than 50K”, “50K-100K”, “over 100K”)
3. Nominal : Unordered Groups.
(cat, dog, tiger),(pizza, burger, coke)
#LabelEncoder #OrdinalEncoder
#FeatureEncoding #FeatureEngineering #FeatureTransformation #DataScience #MachineLearning #CategoricalEncoding
In this video I explained how we can encode the Ordinal Categorical features.
I used below Python libraries to encode the features.
- Sklearn.preprocessing.OrdinalEncoder
- Sklearn.preprocessing.LabelEncoder
- Category_encoders.OrdinalEncoder
Python Notebook link : https://github.com/atulpatelDS/Youtub...
#FeatureEncoding #DataScience #MachineLearning