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Answer: Use imputation techniques such as Alternating Least Squares (ALS) to estimate missing values., Replace null values with the mean or median of the existing data., Introduce a special category to represent null values.
Handling null values in numeric features requires careful consideration to maintain data integrity. The correct answer, using imputation techniques like ALS (C), is effective for estimating missing values based on other data features. Replacing nulls with the mean or median (E) is a straightforward approach but may be skewed by outliers. Introducing a special category for nulls (D) can be meaningful if missingness carries information. Dropping records (A) risks data loss and bias, while using the mode (B) or random samples (F) may not always preserve data distribution. The choice depends on data characteristics and analysis goals.
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What are effective methods for handling null values in numeric features?
A
Drop any records containing nulls to simplify the dataset.
B
Replace null values with the mode of the feature.
C
Use imputation techniques such as Alternating Least Squares (ALS) to estimate missing values.
D
Introduce a special category to represent null values.
E
Replace null values with the mean or median of the existing data.
F
Impute missing values with random samples from the existing data.