Applying CHAID for logistic regression diagnostics and classification accuracy improvement
نویسندگان
چکیده
منابع مشابه
Penalizied Logistic Regression for Classification
Investigation for using different penalty functions (L1 absolute value penalty or lasso, L2 standard weight decay or ridge regression, weight elimination etc.) on the weights for logistic regression for classification. 5 data sets from UCI Machine Learning Repository were used.
متن کاملLogistic regression for graph classification
In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression on graphs. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics. Our method is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition.
متن کاملRobust Logistic Regression and Classification
We consider logistic regression with arbitrary outliers in the covariate matrix. We propose a new robust logistic regression algorithm, called RoLR, that estimates the parameter through a simple linear programming procedure. We prove that RoLR is robust to a constant fraction of adversarial outliers. To the best of our knowledge, this is the first result on estimating logistic regression model ...
متن کاملLogistic Regression for Single Trial EEG Classification
We propose a novel framework for the classification of single trial ElectroEncephaloGraphy (EEG), based on regularized logistic regression. Framed in this robust statistical framework no prior feature extraction or outlier removal is required. We present two variations of parameterizing the regression function: (a) with a full rank symmetric matrix coefficient and (b) as a difference of two ran...
متن کاملLogistic Regression Classification for Uncertain Data
Logistic regression (LR) is a famous classification technique commonly used in statistics, machine learning, and data mining area of knowledge for learning a response of binary nature. It assumes that the data values are pre-determined precisely, but this is not true for all conditions. Uncertainty data arises in many applications because of data collection methodology as in repeated measures, ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Targeting, Measurement and Analysis for Marketing
سال: 2010
ISSN: 1479-1862
DOI: 10.1057/jt.2010.3