Step Snap 1 [Understanding One-hot vs Multi-hot Encoding]

1. One-hot Encoding: The Single Choice Game "Think of one-hot encoding like a multiple-choice quiz where you can only choose ONE answer. Let's say you're answering: 'What's your favorite color?'

Question: Favorite Color →  Red   Blue  Green
Answer:   Red           →   1     0     0
Answer:   Blue          →   0     1     0
Answer:   Green         →   0     0     1

Just like in the quiz, you can only pick one color, so only one '1' appears in your encoding."

1. Multi-hot Encoding: The Multiple Choice Game "Now, multi-hot encoding is like answering: 'What languages do you speak?' Here, you can choose MULTIPLE answers!

Question: Languages →  English  Spanish  French
Answer: [English, Spanish]    →    1        1       0
Answer: [French]             →    0        0       1
Answer: [All three]          →    1        1       1

Just like being multilingual, you can have multiple '1's in your encoding!"

Key Differences:

• One-hot: One choice only → One '1' in the encoding
• Multi-hot: Multiple choices allowed → Multiple '1's possible in the encoding

Real-world Examples:

1. One-hot Use Case:
   • Marital status (Single, Married, Divorced)
   • Blood type (A, B, AB, O)
   • Current occupation (can only have one at a time)
2. Multi-hot Use Case:
   • Movie genres (Action, Comedy, Drama)
   • Food allergies (Nuts, Dairy, Eggs)
   • Skills on a resume (Python, SQL, Java)

This encoding transformation helps convert categorical data into a format that machine learning models can understand and process effectively!

Step Snap 2: [Understanding BigQuery ML Preprocessing Functions]

1. Types of Preprocessing Functions: The Data Processing Team