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R Statistical Application Development by Example Beginner's Guide by Prabhanjan Narayanachar Tattar

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Summary

We started with a simple linear regression model for the binary classification problem and saw the limitedness of the same. The probit regression model, which is an adaption of the linear regression model through a latent variable, overcomes the drawbacks of the straightforward linear regression model. The versatile logistic regression model has been considered in details and we considered the various kinds of residuals that help in the model validation. The influential and leverage point detection has been discussed too, which helps us build a better model by removing the outliers. A metric in the form of ROC helps us in understanding the performance of a classifier. Finally, we concluded the chapter with an application to the important ...

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