Q: Correlation among Hyperparameters of Classifiers I am wondering whether there exists some correlation among the hyperparameters of two different classifiers.
For example: let us say that we run LogisticRegression on a dataset with best hyperparameters (by finding through GridSearch) and want to run another classifier like SVC (SVM classifier) on the same dataset but instead of finding all hyperparameters using GridSearch, can we fix some values  (or reduce range to limit the search space for GridSearch) of hyperparameters?
As an experimentation, I used scikit-learn's classifiers like LogisticRegression, SVS, LinearSVC, SGDClassifier and Perceptron to classifiy some well know datasets. In some cases, I am able to see some correlation empirically, but not always for all datasets.
So please help me to clear this point.

A: I don't think you can correlated different parameters of different classifiers together like this. This is mainly because each classifier behaves differently as it has it's own way of adjusting the data along their own set of equations. For example, take the case of SVC with two different kernels rbf and sigmoid. It might be the case that rbf may fit perfectly over the data with the intercept parameter C set to say 0.001, while 'sigmoidkernel over the same data may fit withC` value 0.00001. Both values may also be equal. However, you can never say that for sure. When you say that :

In some cases, I am able to see some correlation empirically, but not always for all datasets.

It may simply be a coincidence. Since it all depends on the and the classifiers. You cannot apply it globally.Correlation does not always equal to causation
You can visit this site and see for yourself that although different regressor functions have the same parameter a, their equations are vastly different and hence over the same dataset you might drastically different values of a.
