Decision Trees in Python
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 Python. 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 Python. 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
- Understand concepts & mathematics of decision trees
- Implement, evaluate & optimize the decision tree model
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 but not mandatory.
SYLLABUS & TOPICS COVERED
- Decision Trees
- Decision trees use cases and theory behind it
- Data transformation necessary for decision trees
- Implementation of decision trees on a dataset
- Model performance evaluation and tuning
SOFTWARE REQUIREMENTS
You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.
