Including Scaling and PCA as parameter of GridSearchCV

I want to run a logistic regression using GridSearchCV, but I want to contrast the performance when Scaling and PCA is used, so I don’t want to use it in all cases. I basically would like to include …

Tuning the hyperparameter with gridsearch results in overfitting

Tuning the hyperparameter with gridsearch results in overfitting. The train error is definitely low, but the test error is high. Can’t you adjust the hyperparameter to lower the test error? before tuning train_error: 0.386055, test_error: 0.674069 -after tuning train_error: 0.070645, test_error: 0.708254 Answer It all depends on the data you are training. If the data you are using for training is quite less, let’s say 500 rows and a few columns and even then you are trying to split into training and testing data. The XGBoost is most likely to overfit on the training data. To make sure your model

Cross validation with grid search returns worse results than default

I’m using scikitlearn in Python to run some basic machine learning models. Using the built in GridSearchCV() function, I determined the “best” parameters for different techniques, yet many of these perform worse than the defaults. I include the default parameters as an option, so I’m surprised this would happen. For example: This is the same as the defaults, except max_depth is 3. When I use these parameters, I get an accuracy of 72%, compared to 78% from the default. One thing I did, that I will admit is suspicious, is that I used my entire dataset for the cross validation.