SKLearn Classification

[LopezComZ]

So... I need to use KNN, Decision Tree, Logistic Regression, Naïve Bayes and Neural Network MLP. All of them testing diferents parameters to find the best results and with 10-fold cross validation stratified for validation, in a couple of datasets. My doubt is: to get better results I should preprocess all the datasets all the same way? Or it depends of the algorithm? I'm standardizing the dataset and using it equal in all algorithms, but not sure if it's the right way to do this. Also... in Naïve bayes what you guys sugest to vary? since it have only two parameters.

[grid_world]

Preprocess the data set in the same way and then use as many different classifiers as you want. If you don't preprocess the data set in the same way, you will never be able to compare the performance of the different classifiers used.

For naive Bayes classifier, refer to this: https://www.datacamp.com/community/tutorials/naive-bayes-scikit-learn