Decision tree models in Python
What you'll learn
- Decision trees and how they work for regression and classification
- Random forest
- Extremely randomized trees
- Gradient Boosting Decision Trees
- Python programming language
In this practical course, we are going to focus on the decision tree machine learning models using Python programming language.
Decision trees are a particular and very effective type of model of the machine learning landscape. They try to predict the output variable according to particular binary decision rules to apply to the features. The best split that satisfies the rule is found during the training phase.
Decision trees express their best power when used in an ensemble. This way, we get models like random forest and extremely randomized trees (if we use bagging) and gradient boosting decision trees (if we use boosting).
With this course, you are going to learn:
Theory of the decision trees, with several splitting criteria for regression and classification
Hyperparameters of the decision trees
Random forest and its hyperparameters
Extremely randomized tree and its hyperparameters
Gradient Boosting Decision Tree and its hyperparameters
XGBoost and its hyperparameters
All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.
This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the larger course.
Who this course is for:
- Python developers
- Data Scientists
- Computer engineers
My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.
I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.
I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).
I've written many articles about Machine Learning, R and Python and I've been a Top Writer on Medium in Artificial Intelligence category.