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Mastering Hyperparameter Optimization for Machine Learning

Grid Search Using Random Forest Algorithm

Explore how to fine-tune the random forest algorithm by applying the grid search method to find the best hyperparameter combination. Understand how to implement grid search in Python, measure model performance with cross-validation, and improve the F1 score beyond default settings.

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A practical example of the grid search method

This is another example of how we can use the grid search method to optimize the hyperparameters of the ML model.

In this example, we’ll use the random forest algorithm to determine which combination of hyperparameter values will produce the best results compared to the results obtained by using the default values for the hyperparameters.

What will we learn?

In this lesson, we’ll learn how to do the following things in the Jupyter Notebook:

  • Create and train an ML model (random forest algorithm).

  • Measure the performance of the ML model.

  • perform the necessary steps to implement the grid search method.

  • Identify the combination of hyperparameters that provide the best results.

Import important packages

First, we import the important Python packages that will do the following tasks:

  • Create and train an ML model (random forest algorithm).

  • Check the ML model performance.

  • Implement the grid search method.

  • Identify the combination of hyperparameters that provide the best results.

Python 3.8
# import important modules
import numpy as np
import pandas as pd
# sklearn modules
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import MinMaxScaler
import warnings
warnings.filterwarnings("ignore")
# seeding
np.random.seed(123)

Note: The procedure for dataset preparation has ...