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Question 1 of 8
What is the primary purpose of data imputing in machine learning?
To remove all incomplete data points
To estimate missing values in a dataset
To increase the size of the dataset
To simplify complex datasets
Question 2 of 8
In which scenario might data imputing NOT be appropriate?
When the dataset is small
When missing values are informative
When all variables are numerical
When the model requires normalized data
Question 3 of 8
What is a potential risk of imputing missing data?
It always decreases model performance
It can introduce unrealistic patterns
It always removes important information
It makes the dataset too large to process
Question 4 of 8
How can data imputing potentially improve a machine learning model?
By increasing the dataset size
By removing all outliers
By making the model more complex
By increasing the statistical power of the model
Question 5 of 8
Which imputation technique is most suitable for numerical data with a normal distribution?
Mode
Median
Mean
K Nearest Neighbors
Question 6 of 8
In a time series dataset, which method would be most appropriate for filling in missing values?
Mode imputation
Forward fill
Linear regression
Fixed value
Question 7 of 8
When dealing with skewed data or data containing outliers, which imputation method is most robust?
Interpolation
Question 8 of 8
Which imputation technique is best suited for estimating missing values based on multiple features or attributes?
Backward fill