TSPE Method Using Histogram-Based Gradient Boosting

In this example, we’ll use the histogram-based gradient boosting 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 Jupyter Notebook:

• Create and train the histogram-based gradient boosting algorithm.

• Measure the performance of the ML model.

• Perform the steps required to implement the TPE 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 the histogram-based gradient boosting algorithm.

• Check the ML model’s performance.

• Implement the TPE method.

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

Note: The procedure for dataset preparation has been explained in detail in the Data Preparation lesson. Please refer to the lesson to gain insights into how the data was prepared.

Train a base ML model

In this example, we’ll train the histogram-based gradient boosting algorithm with default hyperparameter values using the ...