Decision Trees, Random Forests, AdaBoost & XGBoost in R Studio
In this free online course, learn about the techniques and processes involved in decision trees and ensemble methods.
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Do you want to be an expert in using regression and classification decision trees to solve business problems? This course covers the basics of R and RStudio and the inbuilt datasets of R, including all the steps you should take when using RStudio’s decision trees for solving business problems. Decision trees are prevalent among data scientists looking for a support tool that affords them the ability to make sustainable business decisions. Although a simple decision tree has some accuracy drawbacks, the good news is that advanced variants and techniques can help increase its accuracy, which you will learn about in this course. You will learn the advanced methods suitable for scenarios where the goal is to achieve maximum accuracy at the expense of model interpretability. By learning to use a decision tree, you will be able to segment populations and regions and identify the different predictor variables.
You will be introduced to the different ways of inputting data in R and the parameter used to create a specific amount of buckets in a histogram. Then, you will find out that machine learning refers to the programming of a computer to maximize or minimize performance criteria based on past data. The machine uses this past data to improve its effectiveness in performing a task. Various organizations use this information to formulate business strategies and make effective business decisions using predictive and prescriptive models. You will understand that we assume the functional form of the relationship between the predictor and the predicted variable in the parametric approach to machine learning. In contrast, in the non-parametric process, we do not assume any functional form of the relationship between the predictor and the predicted variable. The course will discuss the steps in building the formulation of the machine learning model.
Next, you will learn how to use a regression tree for continuous quantitative target variables and a classification tree for discrete categorical target variables. The three advanced prediction methods that make it possible to analyze a group based on trees (bagging, random forest and boosting) will also be discussed. The most important criterion to consider when creating a model is to have sound business knowledge of the problem you are trying to solve. You will then understand that the time spent preparing your data will impact the performance of the model. Understanding the factors that influence interest variables will give you a solid foundation in your efforts to create effective models. Finally, you will study missing value imputation in R, univariate analysis and EDD, dummy variable creation and the correlation matrix. This course will be of interest to business analysts, executives or students interested in learning about decision trees. Why wait? Start this course today and become a problem-solving expert.Start Course Now
Machine Learning and Decision Trees
Machine Learning and Decision Trees - Learning Outcomes
Getting Started with R and R Studio
Simple Decision Trees
Creation and Pruning of Decision Trees
Simple Classification Tree
Machine Learning and Decision Trees - Lesson Summary
Ensemble Techniques and Data Analysis
Ensemble Techniques and Data Analysis - Learning Outcomes
Bagging and Random Forest Techniques
Boosting Ensemble Technique
Outlier Treatment in R
Ensemble Techniques and Data Analysis - Lesson Summary
Upon successful completion of this course, you should be able to:
- Discuss the three ways of inputting data in R
- Describe the steps in the process of building a machine learning model
- Explain the different types of decision trees
- List the steps in the decision tree building process
- Discuss the three types of ensemble methods used in decision trees
- Explain the steps in the data exploration process
- Describe the contributors to the expected test error
- Discuss three methods of treating outliers
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