This training course teaches analysts how to predict behaviors, events and make product recommendations based on multiple factors. By the end of the course, students will be able to build classification models and evaluate the accuracy of predictive algorithms. Classification algorithms answer the questions “What is the person or object like?” “What is the likelihood that an event will happen or that a person is part of a particular group?” Applications include fraud detection, cyber attack and intrusion detection, anticipating employee and customer behavior and detecting other threats and events.
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.
Introduction to Classification and Supervised Machine Learning
- Commercial applications of classification models and predictive analytics
- k-Nearest Neighbors
- Association rules
- Decision trees: gini coefficient and information gain
- Random forests
Fine Tuning Your Model
- Confusion matrices and misclassification rates
- Base line errors
- ROC curves
- AUC values
Advanced Classification Algorithms
- Support vector machines
- Logistic regression
- Multivariate logistic regression
- Penalized logistic regression (lasso, ridge, elastic net)
- Naive Bayes
- Linear discrimination analysis
- Additional tips and resources
Supervised Machine Learning: Classification Algorithms Training FAQs
This course is intended for leaders and executives who have a good working knowledge of R, have a good background in basic statistics,
need to build predictive models to anticipate market demand, customer behaviors, fraud and security threats; want to stand out as data scientists with advanced predictive modeling skills.
Students should have taken the Introduction to Data Science, R, and Visualization course or should have the equivalent knowledge of data manipulation, cleaning and visualization. Students should also have take the Regression and Time-Series Analysis course or be familiar with linear regression and basic statistics concepts such as standard deviation, p-values, etc.