query: What is bias in linear regression using NN
positive_passages.docid: 142667
positive_passages.text: Understanding how many biases are there in a neural network?
negative_passages.docid: 102141
negative_passages.text: Hacking linear regression
negative_passages.docid: 84822
negative_passages.text: Other unbiased estimators than the BLUE (OLS solution) for linear models
negative_passages.docid: 16120
negative_passages.text: What is a complete list of the usual assumptions for linear regression?
negative_passages.docid: 74904
negative_passages.text: Random initialization/order in neural network — bias or variance?
negative_passages.docid: 157828
negative_passages.text: Question about bias-variance tradeoff
negative_passages.docid: 58063
negative_passages.text: Assumptions of generalised linear model
negative_passages.docid: 46026
negative_passages.text: How bias arises in using maximum likelihood to determine the variance of a Gaussian?
negative_passages.docid: 117183
negative_passages.text: Deriving the maximum likelihood for the parameters in linear regression
negative_passages.docid: 109585
negative_passages.text: Why Normality assumption in linear regression
negative_passages.docid: 24907
negative_passages.text: Is there a Bayesian interpretation of linear regression with simultaneous L1 and L2 regularization (aka elastic net)?
negative_passages.docid: 29429
negative_passages.text: What algorithm is used in linear regression?
negative_passages.docid: 62345
negative_passages.text: Linear regression: Intercept isn't significant
negative_passages.docid: 130070
negative_passages.text: In linear regression, what does $\beta_1 = 0$ really mean?
negative_passages.docid: 114124
negative_passages.text: Variablity in cv.glmnet results
negative_passages.docid: 133000
negative_passages.text: Ridge regression results different in using lm.ridge and glmnet
negative_passages.docid: 84693
negative_passages.text: Interpreting linear regression with NA for estimates