What are common feature selection methods in regression task?

The correct answer is C. all above.

Correlation coefficient is a measure of the strength of the linear relationship between two variables. It is a number between -1 and 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.

Greedy algorithms are a type of algorithm that makes the locally optimal choice at each step, without considering the global optimum. This can be useful for feature selection, as it can help to find a good set of features without having to search through all possible combinations.

There are many other feature selection methods, but these are two of the most common.

Here are some additional details about each option:

• Correlation coefficient: The correlation coefficient is a measure of the strength of the linear relationship between two variables. It is a number between -1 and 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. The correlation coefficient can be used to identify variables that are correlated with the response variable, which can then be used to select features for a regression model.
• Greedy algorithms: Greedy algorithms are a type of algorithm that makes the locally optimal choice at each step, without considering the global optimum. This can be useful for feature selection, as it can help to find a good set of features without having to search through all possible combinations. One common greedy algorithm for feature selection is the forward selection algorithm. This algorithm starts with an empty set of features and then adds features one at a time, based on how much they improve the model’s performance. The algorithm stops adding features when it reaches a point where adding any more features does not improve the model’s performance.
• All above: Both correlation coefficient and greedy algorithms are common feature selection methods in regression tasks.