Predict Diabetes with a Random Forest using R

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En este proyecto guiado, tú:

Complete a random Training and Test Set from one Data Source using an R function.

Practice data distribution using R and ggplot2.

Apply a Random Forest model.

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

In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

Random ForestComputer ProgrammingR ProgrammingModelling

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. Task 1: In this task the Learner will be introduced to the Course Objectives, which is to how to execute a Random Forest Model using R and the Pima Indians data set. There will be a short discussion about the Interface and an Instructor Bio.

  2. Task 2: The Learners will get experience looking at the data using ggplot2. This is important in order for the practitioner to see the balance of the data, especially as it relates to the Response Variable.

  3. Task 3: The Learner will get experience creating Testing and Training Data Sets. There are multiple ways to do this and the Instructor will go over two of them in this Task.

  4. Task 4: The Learner will get experience with the syntax of the Caret, an R package. There will be a discussion on how you can apply hundreds of algorithms to a single problem using the same syntax using Caret as well.

  5. Task 5: The Learner will get experience evaluation models in this Task. RMSE will be discussed as well as the Confusion Matrix. The conclusion of the course will use the two evaluation metrics see how well the model performed on the test data set.

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

Instructor

Reseñas

Principales reseñas sobre PREDICT DIABETES WITH A RANDOM FOREST USING R

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Preguntas Frecuentes

Preguntas Frecuentes

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