You are a machine learning specialist for a research firm.
Your team uses Amazon SageMaker and its built-in scikit-learn library for feature transformation in your machine learning process.
When using the SimpleImputer transformer to replace missing values in your observations, which strategy is the default strategy that your SageMaker scikit-learn code will use if you don't explicitly pass a strategy parameter?
Click on the arrows to vote for the correct answer
A. B. C. D. E.Answer: D.
Option A is incorrect.
The default strategy is mean.
The constant strategy replaces the missing values with a constant you supply.
Option B is incorrect.
The default strategy is mean.
The most_frequent strategy replaces the missing values with the most frequent value along each column.
Option C is incorrect.
The default strategy is mean.
The median strategy replaces the missing values with the median along each column.
Option D is correct.
The default strategy is mean.
The mean strategy replaces the missing values with the mean along each column.
Option E is incorrect.
There is no mode strategy in the SimpleImputer scikit-learn transformer.
Reference:
Please see the Amazon Machine Learning blog titled Preprocess input data before making predictions using Amazon SageMaker inference pipelines and Scikit-learn.
Sure, I can explain the answer to your question in detail.
When using the SimpleImputer transformer in scikit-learn, it is used to replace missing values in observations. The SimpleImputer transformer can be used with various strategies such as constant, most_frequent, median, and mean.
If you do not explicitly pass a strategy parameter to SimpleImputer in scikit-learn, the default strategy used is "mean". In other words, if you do not specify the strategy parameter, scikit-learn assumes that you want to replace missing values with the mean value of the feature.
Here's an example of how you would use SimpleImputer in scikit-learn to replace missing values with the mean value of the feature:
pythonfrom sklearn.impute import SimpleImputer # create an instance of SimpleImputer imputer = SimpleImputer() # fit the imputer to the data and transform it X_train_imputed = imputer.fit_transform(X_train) In this example, the SimpleImputer transformer is instantiated without passing any parameters, which means that the default strategy of "mean" is used. The fit_transform method is then called on the imputer object, which fits the transformer to the training data and applies the transformation to replace missing values with the mean value of the respective feature.
In conclusion, the correct answer to your question is D. mean, as it is the default strategy used by SimpleImputer in scikit-learn when you do not explicitly pass a strategy parameter.