In supervised learning, Decision Trees (DTs) are a non-parametric technique useful for both classification and regression. The goal is to construct a model that can predict the value of a target variable using simple decision rules learned from the data features. A tree can be thought of as a piecewise constant approximation.
Here are the best decision trees resources for 2022:
Build Decision Trees, SVMs, and Artificial Neural Networks – Coursera
Machine learning algorithms come in a wide variety of forms. Each one has features that may make it better or worse at solving a particular problem. For example, decision trees and support vector machines (SVMs) are algorithms that can solve regression and classification problems, but they are used differently. In the same way, artificial neural networks (ANNs) are used in a more advanced form of machine learning called “deep learning” to solve problems like these and more. You must know how to use all these algorithms to choose the best tool for the job.
Decision Trees, Random Forests, AdaBoost & XGBoost in Python – Udemy
This course goes over all the steps that should be taken when using a Decision tree to solve a business problem. Most courses only teach how to run the analysis, but we think what happens before and after running the analysis is even more critical. For example, you must ensure you have the correct data and do some pre-processing before running the analysis. And once you’ve done the research, you should be able to tell how good your model is and figure out how to use the results to help your business.
Decision trees on CodeAcademy
The CodeAcademy course teaches developers how to build and use decision trees and random forests. The course looks at Gini impurity and Information Gain in detail. The course is primarily an interactive platform that will help developers learn about the ideas and how to code them. The topics discussed are decision trees, Gini impurity, recursive tree building, information gain, and data classification. The concepts are tested on datasets to make different decision trees. There are also portfolio projects and quizzes in the course.
Decision Tree In Python – Simplilearn
The detailed machine learning playlist on Simplilearn has a few modules for decision trees and random forest algorithms. These are lessons 12 and 13, respectively. The lesson has a half-hour video that explains things, as well as texts, diagrams, and charts. In Python, you will learn basic concepts, applications, terms, methodologies, algorithms, and how to build decision trees (DTs).
Decision Trees – Cloud Academy
This training starts with explaining what Distributed Machine Learning is and how it works. Then, you will learn about why and when you might want to train your machine-learning model in a distributed environment.
Machine Learning with Tree-Based Models in Python
This course will teach you how to train decision trees and tree-based models using Python and the accessible sci-kit-learn machine-learning library. You will comprehend the benefits and drawbacks of trees and demonstrate how ensembling can mitigate these drawbacks while practising with real-world datasets. You will also understand how to tune the most influential hyperparameters to maximize the performance of your models.
Source: indiaai.gov.in
