Skip to main content

What are the different types of encoding in Machine Learning?

In machine learning, encoding is a process of converting categorical data (data that represents categories or labels) into a numerical format that can be used for training machine learning models. There are several types of encoding techniques commonly used in ML:

  1. Label Encoding:

    • Label Encoding assigns a unique integer to each category or label.
    • It is suitable for ordinal categorical data where there is a natural order among the categories.
    • Example: Converting "Low," "Medium," and "High" to 0, 1, and 2.

  2. One-Hot Encoding:

    • One-Hot Encoding creates binary columns (often called dummy variables) for each category.
    • It's suitable for nominal categorical data where there is no inherent order among the categories.
    • Example: Converting colors "Red," "Green," and "Blue" into three binary columns.

  3. Ordinal Encoding:

    • Ordinal Encoding is used when there's an ordinal relationship between categories, meaning one category is "greater" or "lesser" than others.
    • It assigns numerical values in a way that preserves the ordinal relationship.
    • Example: Converting "Low," "Medium," and "High" to 1, 2, and 3, respectively.

  4. Binary Encoding:

    • Binary Encoding combines aspects of both Label Encoding and One-Hot Encoding.
    • It converts each unique category into binary code and stores them as separate columns.
    • Example: Converting "Red," "Green," and "Blue" into binary columns.

  5. Count Encoding:

    • Count Encoding replaces categories with the count of their occurrences in the dataset.
    • It can be useful when the frequency of a category is relevant information.
    • Example: Replacing categories with the count of occurrences.

  6. Frequency Encoding:

    • Similar to Count Encoding, Frequency Encoding replaces categories with their frequency of occurrence.
    • It can be beneficial when you want to capture the probability distribution of categories.
    • Example: Replacing categories with their frequency of occurrence.

  7. Target Encoding (Mean Encoding):

    • Target Encoding involves replacing each category with the mean of the target variable for that category.
    • It is often used in classification problems when dealing with categorical targets.
    • Example: Replacing categories with the mean of the target variable for each category.

  8. Hash Encoding:

    • Hash Encoding uses a hash function to map categories to numerical values.
    • It can be useful for handling high-cardinality categorical features.
    • Example: Hashing categories into numerical values.

  9. Backward Difference Encoding:

    • Backward Difference Encoding is used for ordinal categorical data.
    • It represents each level as the difference between it and the previous level.
    • Example: Encoding ordinal data using backward difference encoding.

The choice of encoding method depends on the nature of your data, the machine learning algorithm you plan to use, and the specific problem you're trying to solve. It's essential to choose the appropriate encoding technique to avoid introducing bias or unnecessary complexity into your models.

Labels:

Comments

Post a Comment

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...
Post a Comment
Read more

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
Post a Comment
Read more

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data...
Post a Comment
Read more