Suggested Answer:C🗳️
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function. Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario. Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail. Incorrect Answers: B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not). Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules
This can be a tricky question if you did not read the case study. It says in the case study that relationship between features should be assess using non-parametric statistics. Thus, the reason for using the spearman's rank correlation.
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much “knowledge” one can gain of a certain variable by knowing the value of another variable so it can't be mututal information spearman is the right answer
The question mentions "feature extraction," but the given answer choices are all feature selection methods. If the question is about feature extraction, some commonly used methods are Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF).
If the question is about feature selection, then the most appropriate method depends on the specific characteristics of the data and the problem at hand. Without more information about the specific characteristics of the data and the problem at hand, it is not possible to determine which method is the best choice.
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much “knowledge” one can gain of a certain variable by knowing the value of another variable so it can't be mututal information spearman is the right answer
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Pearson is linear while spearman and kendall are monotonic. Pearson does not work well with outliers but there is not indication that the medianvalue and AvgRoomsInHouse columns have outliers.
Fisher linear discriminant (FLD) analysis is not a valid option (fisher score is)
See links for difference
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection
The answer I think is Pearson's correlation.
The answer should be Fisher Linear Discriminant Analysis, as this is the only method in the given options, that is used for extracting features in low dimensions.
Fisher LDA is definitely not the answer as it is used in classification problems not regression.
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