You have a dataset where the variable 'age' has a few instances of '150', which is an obvious data entry error. What would be the most suitable method to handle these outliers?
- Removal
- Binning
- Transformation
- nan
In this case, removal is the best option as these data points clearly result from data entry errors and don't represent real ages.
You're analyzing a data set and observe a point that significantly deviates from the overall pattern of the data. What role does this point play in your analysis?
- An anomaly
- An outlier
- Both An anomaly and An outlier
- Neither An anomaly nor An outlier
This point would be considered an outlier. Outliers are observations that deviate so much from other observations as to arouse suspicion that they were generated by a different mechanism.
How does a high kurtosis value in a data set impact the Z-score method for outlier detection?
- It decreases the number of detected outliers
- It does not impact the detection of outliers
- It improves the accuracy of outlier detection
- It increases the number of detected outliers
A high kurtosis value means that the data has heavy tails or outliers. This can impact the Z-score method by increasing the number of detected outliers as Z-score is sensitive to extreme values.
While using regression imputation, you encounter a situation where the predicted value for the missing data is outside the expected range. How might you resolve this issue?
- Constrain the predictions within the expected range
- Ignore the problem
- Transform the data
- Use a different imputation method
When the predicted value for missing data is outside the expected range, you might want to constrain the predictions within the expected range. By setting logical bounds, you can make sure that the imputed values are consistent with the known characteristics of the data.
What is skewness in the context of data analysis?
- The asymmetry of the distribution.
- The peak of the distribution.
- The range of the distribution.
- The symmetry of the distribution.
Skewness refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve of a data distribution is skewed to the left or to the right, it means the data are asymmetrical.
You are given a dataset with several missing values that are missing at random. You decided to use multiple imputation. What steps will you follow in applying this method?
- Create several imputed datasets, analyze separately, then average results
- Create several imputed datasets, analyze them together, then interpret results
- Impute only once, then analyze
- Impute several times using different methods, then analyze
The correct approach for multiple imputation is to create several imputed datasets, analyze them separately, and then combine the results. This accounts for the uncertainty around the missing values and results in valid statistical inferences.
You are analyzing a dataset with a high degree of negative skewness. How might this affect your choice of machine learning model?
- It might lead to a preference for models that are based on median values.
- It might lead to a preference for models that are not sensitive to outliers.
- It might lead to a preference for models that are sensitive to outliers.
- It would not affect the choice of the machine learning model.
A high degree of negative skewness indicates the possibility of extreme values towards the negative end of the distribution. This might influence the choice of machine learning models, preferring those that are not sensitive to outliers, such as tree-based models, or those that make fewer assumptions about the data distribution.
In what way does improper handling of missing data affect regularization techniques in a machine learning model?
- Depends on the regularization technique used.
- Does not impact regularization.
- Makes regularization less effective.
- Makes regularization more effective.
If missing data are not handled correctly, it can skew the model's learning and affect its complexity, making regularization techniques (which aim to control model complexity) less effective.
Mishandling missing data can lead to a high level of ________, impacting model performance.
- bias
- precision
- recall
- variance
If missing data is handled improperly, it can lead to biased training data, which can cause the model to learn incorrect or irrelevant patterns and, as a result, adversely affect its performance.
How does multiple imputation handle missing data?
- It deletes rows with missing data
- It estimates multiple values for each missing value
- It fills missing data with mode values
- It replaces missing data with a single value
Multiple imputation estimates multiple values for each missing value, instead of filling in a single value for each missing point. It reflects the uncertainty around the true value and provides more realistic estimates.