Explore the fundamental stages of data transformation crucial for effective model training. Learn how cleaning, scaling, encoding, and engineering refine AI model performance.
Table of Contents
Question
What are the four typical steps of data transformation for model training?
A. Feature selection
B. Finding and removing data outliers
C. Split data
D. Impute missing values
E. ML algorithm selection
F. Normalize numeric features
Answer
A. Feature selection
B. Finding and removing data outliers
D. Impute missing values
F. Normalize numeric features
Explanation
After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation such as Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.
Split data is coming after data transformation.
ML algorithm selection data is coming after data transformation and Split Data steps.
After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation: Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.
Option C is incorrect because Split data is coming after data transformation.
Option D is incorrect because ML algorithm selection data is coming after data transformation and Split Data steps.
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