DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle Glossary Bibliography Index

Logistic Regression

Linear regression is a successful framework for building models. However, not all data fits the assumptions underlying linear regression.

Logistic Regression is appropriate when the target variable is binary. It is used to build a linear model involving the input variables to predict a transformation of the target variable, in particular, the logit function, which is the natural logarithm of what is called the ``odds'' ( $log(\frac{p}{1-p}$).

The inverse of the logit function is the logistic function, $\frac{1}{1+e^{-z}}$, to return the results of the linear model back to the 0-1 range.

We can see the effect of the logistic function in the following plot. Essentially, it maps numbers from a range from minus infinity to plus infinity, to the range 0 to 1.

http://rattle.togaware.com/code/rplot-logistic.R

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