k-fold cross-validation explained in plain English.

Random forests - An ensemble of decision trees.

Train a regression model using a decision tree.

You can also refresh your memory by reading the following contents previously written by me. It is recommended to have a good knowledge and understanding of machine learning techniques such as cross-validation, machine learning pipelines, etc and algorithms such as decision trees, random forests, etc. I assume that you are already familiar with popular Python libraries such as numpy, pandas, scikit-learn, etc and using Jupyter Notebook and RStudio.

Milestone 7: Building a pipeline with XGBoost.

Milestone 6: Get your data ready for XGBoost.

Milestone 5: XGBoost’s hyperparameters tuning.

Milestone 4: Evaluating your XGBoost model through cross-validation.

Milestone 2: Classification with XGBoost.

I will occasionally use R where important. When implementing the algorithm, the default programming language is Python. When you complete the journey through all milestones, you will have a good knowledge and hands-on experience (implementing the algorithm effectively using R and Python) in the followings. Here, each subtopic is called a milestone. We discuss the entire topic step by step. It is like a journey, maybe a long journey for newcomers. The topic we are discussing is broad and important so that we discuss it through a series of articles. Most people say XGBoost is a money-making algorithm because it easily outperforms any other algorithms, gives the best possible scores and helps its users to claim luxury cash prizes from data science competitions. Welcome to another article series! This time, we are discussing XGBoost (Extreme Gradient Boosting) - The leading and the most preferred machine learning algorithm among data scientists in the 21st century.

Xgboost install linux