Discover why imputing missing values is a critical data cleaning step for computer vision and machine learning, with expert insights into preprocessing techniques and their roles in model accuracy.
Table of Contents
Question
Which step falls under data cleaning?
A. Imputing missing values
B. One-hot encoding
C. Data splitting
D. Tokenization
Answer
A. Imputing missing values
Explanation
Imputing missing values (Option A) falls under data cleaning in machine learning workflows. Data cleaning focuses on identifying and correcting errors in datasets, such as handling missing or inconsistent data, removing duplicates, and filtering outliers.
Imputing Missing Values (A)
A core data cleaning task involves addressing gaps in datasets. Missing values can distort model training and reduce accuracy. Techniques like deletion or statistical imputation (mean, median, or regression-based methods) are used to maintain dataset integrity.
Example: In healthcare datasets, missing patient records might be filled using averages from similar demographics to avoid biased analyses.
Why Other Options Are Incorrect
One-Hot Encoding (B): Converts categorical data (e.g., “red,” “blue”) into numerical format for ML algorithms but is a preprocessing step, not cleaning.
Data Splitting (C): Divides data into training/test sets to prevent overfitting but occurs after cleaning.
Tokenization (D): Breaks text into smaller units (words, subwords) for NLP tasks, unrelated to cleaning.
Best Practices for Data Cleaning
Use tools like Encord Active to automate outlier detection and missing value imputation.
Validate data accuracy post-cleaning to ensure robust model performance.
By resolving missing values early, developers ensure reliable inputs for computer vision models, directly impacting their accuracy and generalizability.
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