One-Hot Encoding in CatBoost
One-hot encoding is a common technique used to convert categorical variables into a format that can be provided to machine learning algorithms. In one-hot encoding, each category is represented as a binary vector, where only one element is “1” (indicating the presence of the category) and all other elements are “0”.
One-hot encoding converts categorical variables into a binary matrix, where each category is represented by a separate binary feature, where the corresponding category is marked as ‘True’ or ‘1’, and all other categories are marked with ‘False’ or ‘0’. This is particularly useful for categorical features with a small number of unique values.
Each category is represented as a binary vector.
- Example: For a feature with categories “Red”, “Green”, and “Blue”:
- Red: [1, 0, 0]
- Green: [0, 1, 0]
- Blue: [0, 0, 1]
CatBoost uses one-hot encoding for categorical features with a small number of unique values. The default threshold for applying one-hot encoding depends on various conditions, such as the training mode and the availability of target data. For instance:
- Default Thresholds:
- GPU Training: 255 unique values if the selected Ctr (Categorical Target Statistics) types require target data that is not available during training.
- Ranking Mode: 10 unique values.
- Other Conditions: 2 unique values if none of the above conditions are met.
CatBoost’s Categorical Encoding: One-Hot vs. Target Encoding
CatBoost is a powerful gradient boosting algorithm that excels in handling categorical data. It incorporates unique methods for encoding categorical features, including one-hot encoding and target encoding. Understanding these encoding techniques is crucial for effectively utilizing CatBoost in machine learning tasks.
In real-world datasets, we quite often deal with categorical data. The cardinality of a categorical feature, i.e. the number of different values that the feature can take varies drastically among features and datasets from just a few to thousands and millions of distinct values. The values of a categorical feature can be distributed almost uniformly and there might be values with a frequency different by the orders of magnitude. CatBoost supports some traditional methods of categorical data preprocessing, such as One-hot Encoding and Frequency Encoding. However one of the signatures of this package is its original solution for categorical features encoding.
Table of Content
- One-Hot Encoding in CatBoost
- Target Encoding in CatBoost
- Implementing One-hot encoding and Target encoding in CatBoost
- 1. Implementing One-Hot Encoding in CatBoost
- 2. Demonstrating Target Encoding in CatBoost
- Advantages and Disadvantages of One-Hot Encoding and Target Encoding