Decision Tree Classifier for Beginners in R

ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

Understand the concept of the decision tree algorithm

Build decision tree models

Evaluate the performance of the model

Clock2 hours
BeginnerPrincipiante
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

Welcome to this project-based course Decision Tree Classifier for Beginners in R. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn how to build decision tree models using the tree and rpart libraries in R. We will start this hands-on project by importing the Sonar data into R and exploring the dataset. By the end of this 2-hour long project, you will understand the basic intuition behind the decision tree algorithm and how it works. To build the model, we will divide or partition the data into the training and testing data set. Finally, you will learn how to evaluate the model’s performance using metrics like Accuracy, Sensitivity, Specificity, F1-Score, and so on. By extension, you will learn how to save the trained model on your local system. Although you do not need to be a data analyst expert or data scientist to succeed in this guided project, it requires a basic knowledge of using R, especially writing R syntaxes. Therefore, to complete this project, you must have prior experience with using R. If you are not familiar with working with using R, please go ahead to complete my previous project titled: “Getting Started with R”. It will hand you the needed knowledge to go ahead with this project on Decision Tree. However, if you are comfortable with working with R, please join me on this beautiful ride! Let’s get our hands dirty!

Habilidades que desarrollarás

  • Predictive Modelling
  • Decision Tree
  • Machine Learning
  • Statistical Classification
  • Accuracy And Precision

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Getting Started

  2. Import Required Packages

  3. Import and Explore Dataset

  4. Create Train and Test Sets

  5. Train the decision tree model

  6. Evaluating Model Performance

  7. Wrap up

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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