Finding optimal training parameters using grid search

When you are working with classifiers, you do not always know what the best parameters are. You cannot brute-force it by checking for all possible combinations manually. This is where grid search becomes useful. Grid search allows us to specify a range of values and the classifier will automatically run various configurations to figure out the best combination of parameters. Let's see how to do it.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.metrics import classification_report 
from sklearn import cross_validation, grid_search 
from sklearn.ensemble import ExtraTreesClassifier 
from sklearn import cross_validation 
from sklearn.metrics import classification_report 
 
from utilities import visualize_classifier 

We will use the data available in data_random_forests.txt for analysis:

# Load input data 
input_file...