From the course: NLP with Python for Machine Learning Essential Training

Unlock the full course today

Join today to access over 22,600 courses taught by industry experts or purchase this course individually.

Evaluate random forest model performance

Evaluate random forest model performance - Python Tutorial

From the course: NLP with Python for Machine Learning Essential Training

Start my 1-month free trial

Evaluate random forest model performance

“

- [Instructor] In this lesson, we're going to take a couple concepts that we've learned through the last few lessons: grid search and cross-validation, and we're going to combine them to create a very powerful model tuning and evaluation tool that is often the default tool for tuning and evaluating machine learning models. So, just to recap very quickly, grid search is setting up different parameter settings that you want to test and then exhaustively searching that entire grid to determine the best model. And then cross-validation takes your data set, divides it into k subsets, then you repeat the holdout method where you train on some data set and evaluate it on a separate data set k times. So, in each iteration, you're using a different subset of data as the test set and all the rest of the data as training set. So, combining these ideas into GridSearchCV, what this method will allow you to do is define a grid of parameters that you want to explore and then within each setting, it…

Contents