The reasons could be the controlling parameters of XGBoost.. or the controlling parameters of GA, and PSO algorithms such as the values of Pm, Pc, or C1, C2, and w.
One more thing, it is very common to find a machine learning model with a low performance as compared to other models on a specific case study. Maybe GA and PSO did not help the XGBoost to enhance its performance.
By the way, what is the purpose of using GA / PSO ? what is your optimization problem ? is it "Feature Selection" ?
What is XGBoost-Grid-XGBoost ?!
What does X and Y axis in the image represent ? What is the purpose of applying optimization or feature selection algorithms ?
Hi again!
To echo the questions of the two above:
1) What do x and y represent? Number of features and accuracy?
2) What is the reason behind using GA and PSO specifically for feature selection?
3) Were you interested in using GA for feature selection or hyperparameter optimization? I can't remember!
Ethan G. Armstrong
Again thank you so much!
1) What do x and y represent? Number of features and accuracy?
Ans: X - the value of output; Y - No of observation
2) What is the reason behind using GA and PSO specifically for feature selection?
Ans: sorry it is PCA, not PSO, This is to compare the efficiency (in case of the regression variables dataset) of both; as PCA is used as a benchmark since it is used frequently over the literature
3) Were you interested in using GA for feature selection or hyperparameter optimization?
Ans: GA is used for the feature selection only
thank you so much
best regards!
Suraj
Ritika Lohiya
What does X and Y axis in the image represent ?
Ans: X - the value of output; Y - No of observation
What is the purpose of applying optimization or feature selection algorithms?
Ans: to select the best input variables selection, in this case, 5 (in GA) got the best among 21 input variables
many thanks!
Suraj
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