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What does mean the convergence of algorithm? Please discuss.
MunchCrunch19
Member Posts: 23 Learner III
In my case, I applied Fast forest Quantile Regression (Quantile regression forest) with Random grid hyperparameters optimization. Kindly explain the mentioned algorithm convergence in this regard! Thank you
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Answers
If you are asking about the convergence of an ML algorithm, then the convergence is when the algorithm function will stay in a set error range even though you iterate it several times.
In a simple statement, when a model converges there won't be a significant reduction in model error.
I think in your case, as you are using random search of hyperparameters, your model will iterate for multiple sets of parameters and at some point, it will converge and you won't see much improvement in your model after that converging point.
Varun
https://www.varunmandalapu.com/
Be Safe. Follow precautions and Maintain Social Distancing
I used stratified 10 Fold Cross-validation for the mentioned model and hyperparameters tunning, I used 17 Iteration for each hyperparameter tuning. In the end, I cross-validate the model, Let me show you the hyperparameters results and the Quantile loss for 0.07 Quantile and 0.95 quantile and Average quantile, which I got for each iteration. Please see the Picture attached
Is this the exact question he/she asked?
Can you provide his/her statement? Did he/she ask you to prove convergence?
From an algorithm point of view, I don't have much knowledge about the quantile regression algorithm and I need to take a look at it.
Varun
https://www.varunmandalapu.com/
Be Safe. Follow precautions and Maintain Social Distancing
I have used stratified 10FOLD cross-validation using hyperparameter tuning with random search, after getting the best parameter values through Hyperparameter (Random search) I then used these values in the model and used 10 fold cross-validation and cross-validate the model.
Please Kindly see the process in the Below photos with hyperparameters tuning and without hyperparameter tuning.