Finding optimal training parameters using grid search

When working with classifiers, it is not always possible to know what the best parameters are to use. It is not efficient to use brute force 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 grid_search
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

from utilities import visualize_classifier

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

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