Decision Trees in R
Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in R. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.
4 hours of instruction
Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in R. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.
OBJECTIVES
- Identify opportunities and use cases for decision trees
- Build classification models to anticipate events and behaviors
- Implement decision tree on a dataset and evaluate its results
- Optimize the decision tree by tuning the hyperparameters
PREREQUISITES
Learners must be comfortable using Python to manipulate data, must know how to create basic visualizations and having background on classification use cases is recommended.
SYLLABUS & TOPICS COVERED
- Decision Trees
- Decision trees algorithm use cases and logic behind it
- Implementation of decision trees algorithm on a dataset
- Evaluation of model performance
- Tuning and optimization of the decision tree model
SOFTWARE REQUIREMENTS
You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.
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