XGBoost is an ensemble algorithm popular for its outstanding performance. An ensemble algorithm involves multiple models instead of just one. Ensemble algorithms are of two types:
- Bagging: Here, a result from the algorithm is the average of results from individual models.
- Boosting: Here, we start from a base learner model. Each successive model gets created with better-trained parameters. The learning of new parameters happens through optimization algorithms such as gradient descent.
Next, we will look at the application of XGBoost on a dataset to predict the testing time of a newly manufactured car.
It is a step-by-step guide and you can just follow along:
- Import the required packages:
# In[1]: import os
import pandas ...
