What you would learn in Decision tree models in the Python course?
In this practical course, we will concentrate on the decision tree models for machine learning using Python programming language.
Decision trees are a specific and highly effective model for the machine-learning landscape. They attempt to predict output variables by using specific binary rules to be applied to features. The most efficient split that meets the rule is determined in the training phase.
Decision trees show their most significant potential when used in a group. This is how we can create models such as random forests and highly random trees (if we employ bags) and gradient-boosting the power of the decision tree (if we are using boosting).
In this course, you're going to be taught:
The theoretical basis for the decision tree, with a variety of splitting criteria for regression as well as classification
Hyperparameters of the decision tree
Random Forest and the hyperparameters it has
Extremely random trees and their hyperparameters
Gradient Accelerating the Decision Tree as well as its hyperparameters
XGBoost, as well as its hyperparameters
The lessons in this course begin with a brief introduction, and they finish with a real-world demonstration of the Python programming language and its compelling scikit-learn library. The environment for this course is Jupyter, an industry standard in data science. The entire Jupyter notebooks are available for download. Accessible for download.
The course forms part of the supervised machine Learning using Python Online Course, and you'll be able to find several lessons already in the more extensive course.
Decision trees and how they function to improve classification and regression
Extremely random trees
Gradient Boosting Decision Trees
Download Decision tree models in Python from below links NOW!