منابع مشابه
Outlier Diagnostics in Logistic Regression: A Supervised Learning Technique
The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. Logistic regression is one of the most popular supervised learning technique that is used in classification. Fields like computer vision, image analysis and engineering sciences frequently encounter data with outliers (noise). Presence of outliers in the training sampl...
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The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...
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Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...
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Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1981
ISSN: 0090-5364
DOI: 10.1214/aos/1176345513