Hyperparameter Tuning with Sklearn GridSearchCV and RandomizedSearchCV

Hyperparameter Tuning with Sklearn GridSearchCV and RandomizedSearchCV

About Our Dataset

The data set contains details of bank customer churn. Customer churn refers to when a customer ceases his or her relationship with a company. The goal is to create a machine learning model to predict whether a customer will leave the bank services or not. The dataset consists of 1000 rows and 14 columns in total

 

 

 

 

  • It is as good as the set of hyperparameters provided by you as input. It does not magically search for all possible hyperparameters unless you give them as part of the input.
  • To go through all combinations of hyperparameters  GrisSearchCV can take too much computing resources.

 

LEAVE A REPLY

Please enter your comment!
Please enter your name here