Preparing for RandomizedSearch
Last semester your professor challenged your class to build a predictive model to predict final exam test scores. You tried running a few different models by randomly selecting hyperparameters. However, running each model required you to code it individually.
After learning about RandomizedSearchCV()
, you're revisiting your professors challenge to build the best model. In this exercise, you will prepare the three necessary inputs for completing a random search.
This exercise is part of the course
Model Validation in Python
Exercise instructions
- Finalize the parameter dictionary by adding a list for the
max_depth
parameter with options 2, 4, 6, and 8. - Create a random forest regression model with ten trees and a
random_state
of 1111. - Create a mean squared error scorer to use.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import make_scorer, mean_squared_error
# Finish the dictionary by adding the max_depth parameter
param_dist = {"____": [____],
"max_features": [2, 4, 6, 8, 10],
"min_samples_split": [2, 4, 8, 16]}
# Create a random forest regression model
rfr = ____(____=10, ____=1111)
# Create a scorer to use (use the mean squared error)
scorer = ____(____)