01 One hot encoding (Categorical variable encoding - Python code Machine Learning AI)
In this video, we dive deep into the world of categorical data encoding, focusing on the Basic Encoding Techniques of one-hot encoding. Whether you're new to data science or looking to solidify your understanding, this video will guide you through step-by-step explanations and key concepts. You may want to check out other videos containing 37 other encoding methods, grouped into 8 comprehensive categories, a complete cheat-sheet on how to choose right method and comparative analysis using standard datasets.
Learn how each method works, the advantages and limitations of one-hot encoding. Enhance your knowledge and apply these techniques in your data preprocessing workflows for better performance in machine learning models.
What you'll learn in this video:
• In-depth explanation of one-hot encoding
• Python code implementations for one-hot encoding using:
o Custom logic using Pandas and Numpy
o sklearn library
o category_encoder library
o feature_engine library
o
• When and why to choose each encoding method based on data types
This video is part of a larger series covering advanced categorical encoding methods. Subscribe to stay updated as we release detailed tutorials for all 38 encoding methods!
Methods to cover in this series:
1. Basic Encoding Techniques (Simple transformation of categories into numbers)
• One-hot encoding
• Label encoding
• Ordinal encoding
2. Target-Based Encoding (Encoding based on relationship with the target variable)
• Target Encoding (Mean Encoding)
• Target Mean Encoding with k-fold Cross-validation
• Leave-One-Out Encoding
• Bayesian Encoding
• James-Stein Encoding
• M-estimator Encoding
• Smooth Target Encoding
• Probability Ratio Encoding
3. Frequency or Count Based Encoding (Using counts or frequency of categories)
• Frequency Encoding
• Count Encoding
4. Binary and Hash Encoding Methods (Represent categories in binary or hash form)
• Binary Encoding
• Hash Encoding
• Geohash Encoding
• Gray Encoding
• BaseN Encoding
5. Mathematical or Statistical Encoding (Using mathematical or statistical models)
• Effect Encoding (Deviation Encoding)
• Backward Difference Encoding
• Polynomial Encoding
• Generalized Linear Mixed Models (GLMM)
• Kernel Feature Maps
• Principal Component Encoding (PCA-based encoding)
• Regularized Encoding
• Weight of Evidence (WOE) Encoding
• CatBoost Encoding
6. Decision Tree-Based Encoding (Using decision trees for encoding)
• Decision Tree Encoding
7. Encoding for Special Scenarios (Handling special data types or cases)
• Thermometer Encoding
• Rank Hot Encoding
• Sum Encoding
• Quantile Encoding
• Similarity Encoding
• Time-based Encoding
• Rare Category Encoding
• Entity Embedding
8. Advanced Encoding (More complex or rarely used methods)
• Facet Encoding
• Difference Encoding (Helmert Encoding)
Keywords: categorical encoding, one-hot encoding, data preprocessing, machine learning, data science, categorical variables, feature engineering, encoding techniques, python coding, basic encoding, data transformation, data handling, machine learning models, supervised learning, real-world examples, encoding in python, data processing, AI, machine learning tutorial, data science beginner, ordinal data, nominal data, python tutorial, encoding methods comparison, feature encoding, data science series
• 01 One hot encoding (Categorical variable ...
In questa pagina del sito puoi guardare il video online 01 One hot encoding (Categorical variable encoding - Python code Machine Learning AI) della durata di ore minuti seconda in buona qualità , che l'utente ha caricato Baba's World 22 settembre 2024, condividi il link con amici e conoscenti, su youtube questo video è già stato visto 103 volte e gli è piaciuto 5 spettatori. Buona visione!
