Linked Questions
354 questions linked to/from Algorithms for automatic model selection
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Which method (enter, Forward LR or Backward LR) of logistic regression to use? [duplicate]
My study is a prospective observational study. My dependent variable (outcome) is development of surgical site infection (SSI) after surgery and my independent variables (predictors) are many factors ...
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Main Drawbacks of stepwise regression [duplicate]
People typically prefer the Lasso or other methods to stepwise regression.
What are the main problems in stepwise regression which makes it unreliable
specifically the problems with forward selection ...
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In stepwise regression, how to interpret non-significant variables? [duplicate]
I have more than 15 IVs such as age, gender, education, first language, technology proficiency, health condition, etc, and one of my DVs is health literacy level, which is measured through a standard ...
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How to choose predictor variables for GLM / GLMM from rather large data set? [duplicate]
I have about 80 predictor variables (with some multicollinearity, I assume) and a non-normal count data response variable (n=570) which is arranged into groups (n=34). I need to reduce the number of ...
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p-values for feature selection [duplicate]
I am doing multiple regression analysis, in which i want to eliminate some of the insignificant features. In most of the machine learning books subset selection, shrinkage methods or PCA is used for ...
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Chi Square and t test to select variables for logistic regression [duplicate]
I need to build a logistic regression model. there are around 50 categorical variables. So, is this approach to select variables wrong?: do a chi square test of dependent variable vs independent ...
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Why avoid stepwise regression? [duplicate]
I have been using model averaging and model selection bases on AIC and BIC for a while. I have recently discover the stepwise regression technique and I found a lots of people critize this methods.
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Is forward selection using AIC as selection critiria valid? [duplicate]
I'm using a sequential approach to decide the best fitting model for my data. (I'm still new to R, so I decided to go for a manual approach rather than an automated one offered by R packages).
I'm ...
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Chi square and logistic regression [duplicate]
Before running a binary logistic regression model i was interested to know the strength of association between IV and DV but for some independent variables the results came out to be insignificant.. ...
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Logistic Regression Model Selection Criteria [duplicate]
I'm having a go at coding a logistic regression model building algorithm and I'd appreciate some advice. I've read in several places (including here) that minimizing both AIC and BIC could be an ...
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Beginner - Iteratively adding terms to regression model? [duplicate]
I'm learning about regression models via Andrew Ng's Coursera course. I have a question regarding automatically finding a good model.
Does it make sense (my guess is no) to iteratively add terms, or ...
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Selecting most useful variables from Ordinary Least Squares using statsmodels in python [duplicate]
I have 6 independent x variables and have used OLS to get a polynomial model to describe their relationship with my dependent y variable.
Here is what I get from statsmodels:
...
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Does removing features from a regression model until the p value for all regressors is significant make sense? [duplicate]
I'll start by saying that I'm a software engineer, and while I took statistics courses, I'm far of an expert in it.
My job is to essentially build software for data scientists in my team to help them ...
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what is a good source for linear regression dataset and best model solutions [duplicate]
I'm working on automating the process of getting the best model for linear regression. Does anyone know a good source that has examples of the dataset and best model for linear regression solved so I ...
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Is there an algorithm to determine what type of prediction function to use in linear regression? [duplicate]
I recently started learning machine-learning and just learned the basics of linear regression.
So gradient descent and other optimization algorithms can be used to find the values of θ in the ...
