Linked Questions

91
votes
4answers
40k views

Diagnostic plots for count regression

What diagnostic plots (and perhaps formal tests) do you find most informative for regressions where the outcome is a count variable? I'm especially interested in Poisson and negative binomial models, ...
97
votes
1answer
76k views

Interpreting plot.lm()

I had a question about interpreting the graphs generated by plot(lm) in R. I was wondering if you guys could tell me how to interpret the scale-location and leverage-residual plots? Any comments ...
61
votes
5answers
21k views

On the importance of the i.i.d. assumption in statistical learning

In statistical learning, implicitly or explicitly, one always assumes that the training set $\mathcal{D} = \{ \bf {X}, \bf{y} \}$ is composed of $N$ input/response tuples $({\bf{X}}_i,y_i)$ that are ...
74
votes
3answers
34k views

Diagnostics for logistic regression?

For linear regression, we can check the diagnostic plots (residuals plots, Normal QQ plots, etc) to check if the assumptions of linear regression are violated. For logistic regression, I am having ...
40
votes
4answers
33k views

Logistic Regression - Error Term and its Distribution

On whether an error term exists in logistic regression (and its assumed distribution), I have read in various places that: no error term exists the error term has a binomial distribution (in ...
40
votes
2answers
164k views

Interpreting the residuals vs. fitted values plot for verifying the assumptions of a linear model

Consider the following figure from Faraway's Linear Models with R (2005, p. 59). The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a ...
35
votes
3answers
28k views

Interpreting residual diagnostic plots for glm models?

I am looking for guidelines on how to interpret residual plots of glm models. Especially poisson, negative binomial, binomial models. What can we expect from these plots when the models are "correct"...
28
votes
1answer
8k views

Is there any algorithm combining classification and regression?

I'm wondering if there's any algorithm could do classification and regression at the same time. For example, I'd like to let the algorithm learn a classifier, and at the same time within each label, ...
16
votes
3answers
14k views

hinge loss vs logistic loss advantages and disadvantages/limitations

Hinge loss can be defined using $\text{max}(0, 1-y_i\mathbf{w}^T\mathbf{x}_i)$ and the log loss can be defined as $\text{log}(1 + \exp(-y_i\mathbf{w}^T\mathbf{x}_i))$ I have the following questions: ...
13
votes
2answers
18k views

Bayesian logit model - intuitive explanation?

I must confess that I previously haven't heard of that term in any of my classes, undergrad or grad. What does it mean for a logistic regression to be Bayesian? I'm looking for an explanation with a ...
14
votes
2answers
3k views

Why linear regression has assumption on residual but generalized linear model has assumptions on response?

Why linear regression and Generalized Model have inconsistent assumptions? In linear regression, we assume residual comes form Gaussian In other regression (logistic regression, poison regression), ...
8
votes
2answers
5k views

Where are the residuals in a GLM?

I am just now moving on to GLMs after the standard models. In the standard model, y = Xb + epsilon and epsilon is assumed to be normally distributed. That ...
3
votes
2answers
4k views

Understand Link Function in Generalized Linear Model

I am still trying to learn (may be the terminology issue) what does "link function" mean. For example, in logistic regression, we assume response variable is coming form binomial distribution. The $\...
9
votes
4answers
3k views

How do I interpret a Cox hazard model survival curve?

How do you interpret a survival curve from cox proportional hazard model? In this toy example, suppose we have a cox proportional hazard model on age variable in <...
3
votes
1answer
695 views

Logistic regression and classification: Adjusting or removing decision boundaries [duplicate]

I'm taking Andrew Ng's Machine Learning Course. In the lesson on classification algorithms, he presents the logit function ($\frac{1}{1+e^{-x}}$) and the way it converts parameterized functions to ...

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