The intuition behind logistic regression starts exactly where linear regression stops—solving the problem of estimated values outside the natural domain of our response variable. It start from the typical problem of having a response variable that can pertain to two alternative categories, either zero or one, and the need for some record to one of those categories based on a set of explanatory variables.
- Preface
- Why to Choose R for Your Data Mining and Where to Start
- A First Primer on Data Mining Analysing Your Bank Account Data
- The Data Mining Process - CRISP-DM Methodology
- Keeping the House Clean – The Data Mining Architecture
- How to Address a Data Mining Problem – Data Cleaning and Validation
- Looking into Your Data Eyes – Exploratory Data Analysis
- Our First Guess – a Linear Regression
- A Gentle Introduction to Model Performance Evaluation
- Don't Give up – Power up Your Regression Including Multiple Variables
- A Different Outlook to Problems with Classification Models
- What is classification and why do we need it?
- Logistic regression
- Support vector machines
- References
- Summary
- The Final Clash – Random Forests and Ensemble Learning
- Random forest
- Ensemble learning
- Applying estimated models on new data
- A more structured approach to predictive analytics
- Applying the majority vote ensemble technique on predicted data
- Further references
- Summary
- Looking for the Culprit – Text Data Mining with R
- Sharing Your Stories with Your Stakeholders through R Markdown
- Principles of a good data mining report
- Set up an rmarkdown report
- Develop an R markdown report in RStudio
- Rendering and sharing an R markdown report
- Further references
- Summary
- Epilogue
- Dealing with Dates, Relative Paths and Functions
The intuition behind logistic regression
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