In a Spark MLlib project, you are working with a large dataset and need to perform feature selection to improve the performance of your machine learning model. Which of the following feature selection techniques can be applied in Spark MLlib, and how do they work?

Simulated

Filter methods, which select features based on their individual statistical properties, such as correlation with the target variable.

9.3%

Wrapper methods, which select features by evaluating their performance in a specific machine learning model.

3.5%

Embedded methods, which select features by incorporating them into the model training process.

5.8%

All of the above, as Spark MLlib supports various feature selection techniques.

81.4%

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