What is the best way to optimize the parameters in a Sklearn classifier when I only have a data set with 684 rows and 177 columns, and the column I want to predict has 3 labels?
I know I should split my data into training, validation, and test set, and then find the parameters to train the training set that maximizes a metric in the validation set and use this optimized classifier in the test set. However, when I did this using a decision tree classifier, the parameters that worked best for the validation set showed a worse result in the test set than the default parameters. So what is the best way to find the best parameters in this data set? I do not know if it helps, but my data set is very sparse.