For this part of the process, we will start with a logistic regression model of all the input variables and then narrow down the features with the best subsets. After this, we will try our hand at discriminant analysis and Multivariate Adaptive Regression Splines (MARS).
- Preface
- A Process for Success
- Linear Regression - The Blocking and Tackling of Machine Learning
- Logistic Regression and Discriminant Analysis
- Advanced Feature Selection in Linear Models
- More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
- Classification and Regression Trees
- Neural Networks and Deep Learning
- Cluster Analysis
- Principal Components Analysis
- Market Basket Analysis, Recommendation Engines, and Sequential Analysis
- An overview of a market basket analysis
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- An overview of a recommendation engine
- Business understanding and recommendations
- Data understanding, preparation, and recommendations
- Modeling, evaluation, and recommendations
- Sequential data analysis
- Summary
- Creating Ensembles and Multiclass Classification
- Time Series and Causality
- Text Mining
- R on the Cloud
- R Fundamentals
- Sources
Modeling and evaluation
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