idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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2,301 | What is the meaning of "All models are wrong, but some are useful" | I have just rephrased the above answer by considering process models as focus point. The statement can be interpreted as follows:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models are only a little wrong. They ignore some things, For example: --> changing requi... | What is the meaning of "All models are wrong, but some are useful" | I have just rephrased the above answer by considering process models as focus point. The statement can be interpreted as follows:
"All models are wrong" that is, every model is wrong because it is a s | What is the meaning of "All models are wrong, but some are useful"
I have just rephrased the above answer by considering process models as focus point. The statement can be interpreted as follows:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models are only a litt... | What is the meaning of "All models are wrong, but some are useful"
I have just rephrased the above answer by considering process models as focus point. The statement can be interpreted as follows:
"All models are wrong" that is, every model is wrong because it is a s |
2,302 | What is the meaning of "All models are wrong, but some are useful" | I would like to give another interpretation of the term "useful". Probably not the one Box thought about.
When you have to make decisions, and this is what all information will finally be used for, then you have to measure your success in some form. When talking about decisions with uncertain information, this measure ... | What is the meaning of "All models are wrong, but some are useful" | I would like to give another interpretation of the term "useful". Probably not the one Box thought about.
When you have to make decisions, and this is what all information will finally be used for, th | What is the meaning of "All models are wrong, but some are useful"
I would like to give another interpretation of the term "useful". Probably not the one Box thought about.
When you have to make decisions, and this is what all information will finally be used for, then you have to measure your success in some form. Whe... | What is the meaning of "All models are wrong, but some are useful"
I would like to give another interpretation of the term "useful". Probably not the one Box thought about.
When you have to make decisions, and this is what all information will finally be used for, th |
2,303 | What is the meaning of "All models are wrong, but some are useful" | "All models are wrong, but some are useful". Perhaps it means: We should be doing the best we can with what we know + search for new learning? | What is the meaning of "All models are wrong, but some are useful" | "All models are wrong, but some are useful". Perhaps it means: We should be doing the best we can with what we know + search for new learning? | What is the meaning of "All models are wrong, but some are useful"
"All models are wrong, but some are useful". Perhaps it means: We should be doing the best we can with what we know + search for new learning? | What is the meaning of "All models are wrong, but some are useful"
"All models are wrong, but some are useful". Perhaps it means: We should be doing the best we can with what we know + search for new learning? |
2,304 | What're the differences between PCA and autoencoder? | PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders.
A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means that the $W$ found by AE and PCA won't necessarily be the same - but the subspace spanned by the respective $W$'s will. | What're the differences between PCA and autoencoder? | PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders.
A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means tha | What're the differences between PCA and autoencoder?
PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders.
A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means that the $W$ found by AE and PCA won't necessarily be the same - but t... | What're the differences between PCA and autoencoder?
PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders.
A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means tha |
2,305 | What're the differences between PCA and autoencoder? | As bayerj points out PCA is method that assumes linear systems where as Autoencoders (AE) do not. If no non-linear function is used in the AE and the number of neurons in the hidden layer is of smaller dimension then that of the input then PCA and AE can yield the same result. Otherwise the AE may find a different subs... | What're the differences between PCA and autoencoder? | As bayerj points out PCA is method that assumes linear systems where as Autoencoders (AE) do not. If no non-linear function is used in the AE and the number of neurons in the hidden layer is of smalle | What're the differences between PCA and autoencoder?
As bayerj points out PCA is method that assumes linear systems where as Autoencoders (AE) do not. If no non-linear function is used in the AE and the number of neurons in the hidden layer is of smaller dimension then that of the input then PCA and AE can yield the sa... | What're the differences between PCA and autoencoder?
As bayerj points out PCA is method that assumes linear systems where as Autoencoders (AE) do not. If no non-linear function is used in the AE and the number of neurons in the hidden layer is of smalle |
2,306 | What're the differences between PCA and autoencoder? | The currently accepted answer by @bayerj states that the weights of a linear autoencoder span the same subspace as the principal components found by PCA, but they are not the same vectors. In particular, they are not an orthogonal basis. This is true, however we can easily recover the principal components loading vecto... | What're the differences between PCA and autoencoder? | The currently accepted answer by @bayerj states that the weights of a linear autoencoder span the same subspace as the principal components found by PCA, but they are not the same vectors. In particul | What're the differences between PCA and autoencoder?
The currently accepted answer by @bayerj states that the weights of a linear autoencoder span the same subspace as the principal components found by PCA, but they are not the same vectors. In particular, they are not an orthogonal basis. This is true, however we can ... | What're the differences between PCA and autoencoder?
The currently accepted answer by @bayerj states that the weights of a linear autoencoder span the same subspace as the principal components found by PCA, but they are not the same vectors. In particul |
2,307 | What're the differences between PCA and autoencoder? | The general answer is that auto-associative neural networks can perform non-linear dimensionality reduction. Training the network is generally not as fast as PCA, so the trade-off is computational resources vs. expressive power.
However, there was a confusion in the details, which is a common misconception. It is true ... | What're the differences between PCA and autoencoder? | The general answer is that auto-associative neural networks can perform non-linear dimensionality reduction. Training the network is generally not as fast as PCA, so the trade-off is computational res | What're the differences between PCA and autoencoder?
The general answer is that auto-associative neural networks can perform non-linear dimensionality reduction. Training the network is generally not as fast as PCA, so the trade-off is computational resources vs. expressive power.
However, there was a confusion in the ... | What're the differences between PCA and autoencoder?
The general answer is that auto-associative neural networks can perform non-linear dimensionality reduction. Training the network is generally not as fast as PCA, so the trade-off is computational res |
2,308 | Why is the L2 regularization equivalent to Gaussian prior? | Let us imagine that you want to infer some parameter $\beta$ from some observed input-output pairs $(x_1,y_1)\dots,(x_N,y_N)$. Let us assume that the outputs are linearly related to the inputs via $\beta$ and that the data are corrupted by some noise $\epsilon$:
$$y_n = \beta x_n + \epsilon,$$
where $\epsilon$ is Gauss... | Why is the L2 regularization equivalent to Gaussian prior? | Let us imagine that you want to infer some parameter $\beta$ from some observed input-output pairs $(x_1,y_1)\dots,(x_N,y_N)$. Let us assume that the outputs are linearly related to the inputs via $\b | Why is the L2 regularization equivalent to Gaussian prior?
Let us imagine that you want to infer some parameter $\beta$ from some observed input-output pairs $(x_1,y_1)\dots,(x_N,y_N)$. Let us assume that the outputs are linearly related to the inputs via $\beta$ and that the data are corrupted by some noise $\epsilon$... | Why is the L2 regularization equivalent to Gaussian prior?
Let us imagine that you want to infer some parameter $\beta$ from some observed input-output pairs $(x_1,y_1)\dots,(x_N,y_N)$. Let us assume that the outputs are linearly related to the inputs via $\b |
2,309 | Why is the L2 regularization equivalent to Gaussian prior? | First notice that median minimizes the L1 norm (see here or here for learning more on L1 and L2)
$$ \DeclareMathOperator*{\argmin}{arg\,min}
\text{median}(x) = \argmin_s \sum_i |x_i - s|^1 $$
while mean minimizes L2
$$ \text{mean}(x) = \argmin_s \sum_i |x_i - s|^2 $$
now, recall that Normal distributions' $\mu$ param... | Why is the L2 regularization equivalent to Gaussian prior? | First notice that median minimizes the L1 norm (see here or here for learning more on L1 and L2)
$$ \DeclareMathOperator*{\argmin}{arg\,min}
\text{median}(x) = \argmin_s \sum_i |x_i - s|^1 $$
while m | Why is the L2 regularization equivalent to Gaussian prior?
First notice that median minimizes the L1 norm (see here or here for learning more on L1 and L2)
$$ \DeclareMathOperator*{\argmin}{arg\,min}
\text{median}(x) = \argmin_s \sum_i |x_i - s|^1 $$
while mean minimizes L2
$$ \text{mean}(x) = \argmin_s \sum_i |x_i - ... | Why is the L2 regularization equivalent to Gaussian prior?
First notice that median minimizes the L1 norm (see here or here for learning more on L1 and L2)
$$ \DeclareMathOperator*{\argmin}{arg\,min}
\text{median}(x) = \argmin_s \sum_i |x_i - s|^1 $$
while m |
2,310 | Why is the L2 regularization equivalent to Gaussian prior? | For a linear model with multivariate normal prior and multivariate normal likelihood, you end up with a multivariate normal posterior distribution in which the mean of the posterior (and maximum a posteriori model) is exactly what you would obtain using Tikhonov regularized ($L_{2}$ regularized) least squares with an a... | Why is the L2 regularization equivalent to Gaussian prior? | For a linear model with multivariate normal prior and multivariate normal likelihood, you end up with a multivariate normal posterior distribution in which the mean of the posterior (and maximum a pos | Why is the L2 regularization equivalent to Gaussian prior?
For a linear model with multivariate normal prior and multivariate normal likelihood, you end up with a multivariate normal posterior distribution in which the mean of the posterior (and maximum a posteriori model) is exactly what you would obtain using Tikhono... | Why is the L2 regularization equivalent to Gaussian prior?
For a linear model with multivariate normal prior and multivariate normal likelihood, you end up with a multivariate normal posterior distribution in which the mean of the posterior (and maximum a pos |
2,311 | Why is the L2 regularization equivalent to Gaussian prior? | For a regression problem with $k$ variables (w/o intercept) you do OLS as
$$\min_{\beta} (y - X \beta)' (y - X \beta)$$
In regularized regression with $L^p$ penalty you do
$$\min_{\beta} (y - X \beta)' (y - X \beta) + \lambda \sum_{i=1}^k |\beta_i|^p $$
We can equivalently do (note the sign changes)
$$\max_{\beta} -(y ... | Why is the L2 regularization equivalent to Gaussian prior? | For a regression problem with $k$ variables (w/o intercept) you do OLS as
$$\min_{\beta} (y - X \beta)' (y - X \beta)$$
In regularized regression with $L^p$ penalty you do
$$\min_{\beta} (y - X \beta) | Why is the L2 regularization equivalent to Gaussian prior?
For a regression problem with $k$ variables (w/o intercept) you do OLS as
$$\min_{\beta} (y - X \beta)' (y - X \beta)$$
In regularized regression with $L^p$ penalty you do
$$\min_{\beta} (y - X \beta)' (y - X \beta) + \lambda \sum_{i=1}^k |\beta_i|^p $$
We can ... | Why is the L2 regularization equivalent to Gaussian prior?
For a regression problem with $k$ variables (w/o intercept) you do OLS as
$$\min_{\beta} (y - X \beta)' (y - X \beta)$$
In regularized regression with $L^p$ penalty you do
$$\min_{\beta} (y - X \beta) |
2,312 | Why is the L2 regularization equivalent to Gaussian prior? | To put the equivalence more precisely:
Optimizing model weights to minimize a squared error loss function with L2 regularization is equivalent to finding the weights that are most likely under a posterior distribution evaluated using Bayes rule, with a zero-mean independent Gaussian weights prior
Proof:
The loss functi... | Why is the L2 regularization equivalent to Gaussian prior? | To put the equivalence more precisely:
Optimizing model weights to minimize a squared error loss function with L2 regularization is equivalent to finding the weights that are most likely under a poste | Why is the L2 regularization equivalent to Gaussian prior?
To put the equivalence more precisely:
Optimizing model weights to minimize a squared error loss function with L2 regularization is equivalent to finding the weights that are most likely under a posterior distribution evaluated using Bayes rule, with a zero-mea... | Why is the L2 regularization equivalent to Gaussian prior?
To put the equivalence more precisely:
Optimizing model weights to minimize a squared error loss function with L2 regularization is equivalent to finding the weights that are most likely under a poste |
2,313 | Why is the L2 regularization equivalent to Gaussian prior? | There are two characteristics of Bayesian modeling that need to be emphasized, when discussing the equivalance of certain penalized maximum likelihood estimation and Bayesian procedures.
In the Bayesian framework, the prior is selected based on specifics of the problem and is not motivated by computational expediency.... | Why is the L2 regularization equivalent to Gaussian prior? | There are two characteristics of Bayesian modeling that need to be emphasized, when discussing the equivalance of certain penalized maximum likelihood estimation and Bayesian procedures.
In the Bayes | Why is the L2 regularization equivalent to Gaussian prior?
There are two characteristics of Bayesian modeling that need to be emphasized, when discussing the equivalance of certain penalized maximum likelihood estimation and Bayesian procedures.
In the Bayesian framework, the prior is selected based on specifics of th... | Why is the L2 regularization equivalent to Gaussian prior?
There are two characteristics of Bayesian modeling that need to be emphasized, when discussing the equivalance of certain penalized maximum likelihood estimation and Bayesian procedures.
In the Bayes |
2,314 | Why should I be Bayesian when my model is wrong? | I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.
For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 loans with a few years of quarterly historical data... | Why should I be Bayesian when my model is wrong? | I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.
For instance, my client wan | Why should I be Bayesian when my model is wrong?
I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.
For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 lo... | Why should I be Bayesian when my model is wrong?
I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.
For instance, my client wan |
2,315 | Why should I be Bayesian when my model is wrong? | A very interesting question...that may not have an answer (but that does not make it less interesting!)
A few thoughts (and many links to my blog entries!) about that meme that all models are wrong:
While the hypothetical model is indeed almost invariably and irremediably wrong, it still makes sense to act in an effic... | Why should I be Bayesian when my model is wrong? | A very interesting question...that may not have an answer (but that does not make it less interesting!)
A few thoughts (and many links to my blog entries!) about that meme that all models are wrong:
| Why should I be Bayesian when my model is wrong?
A very interesting question...that may not have an answer (but that does not make it less interesting!)
A few thoughts (and many links to my blog entries!) about that meme that all models are wrong:
While the hypothetical model is indeed almost invariably and irremediab... | Why should I be Bayesian when my model is wrong?
A very interesting question...that may not have an answer (but that does not make it less interesting!)
A few thoughts (and many links to my blog entries!) about that meme that all models are wrong:
|
2,316 | Why should I be Bayesian when my model is wrong? | I only see this today but still I think I should chip in given that I'm kind of an expert and that at least two answers (nr 3 and 20 (thanks for referring to my work Xi'an!)) mention my work on SafeBayes - in particular G. and van Ommen, "Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposa... | Why should I be Bayesian when my model is wrong? | I only see this today but still I think I should chip in given that I'm kind of an expert and that at least two answers (nr 3 and 20 (thanks for referring to my work Xi'an!)) mention my work on SafeBa | Why should I be Bayesian when my model is wrong?
I only see this today but still I think I should chip in given that I'm kind of an expert and that at least two answers (nr 3 and 20 (thanks for referring to my work Xi'an!)) mention my work on SafeBayes - in particular G. and van Ommen, "Inconsistency of Bayesian Infere... | Why should I be Bayesian when my model is wrong?
I only see this today but still I think I should chip in given that I'm kind of an expert and that at least two answers (nr 3 and 20 (thanks for referring to my work Xi'an!)) mention my work on SafeBa |
2,317 | Why should I be Bayesian when my model is wrong? | Edits: Added reference to this paper in the body, as requested by the OP.
I am giving an answer as a naive empirical Bayesian here.
First, the posterior distribution allows you to do computations that you simply cannot do with a straightforward MLE.
The simplest case is that today's posterior is tomorrow's prior. Baye... | Why should I be Bayesian when my model is wrong? | Edits: Added reference to this paper in the body, as requested by the OP.
I am giving an answer as a naive empirical Bayesian here.
First, the posterior distribution allows you to do computations tha | Why should I be Bayesian when my model is wrong?
Edits: Added reference to this paper in the body, as requested by the OP.
I am giving an answer as a naive empirical Bayesian here.
First, the posterior distribution allows you to do computations that you simply cannot do with a straightforward MLE.
The simplest case is... | Why should I be Bayesian when my model is wrong?
Edits: Added reference to this paper in the body, as requested by the OP.
I am giving an answer as a naive empirical Bayesian here.
First, the posterior distribution allows you to do computations tha |
2,318 | Why should I be Bayesian when my model is wrong? | Here are a few other ways of justifying Bayesian inference in misspecified models.
You can construct a confidence interval on the posterior mean, using the sandwich formula (in the same way that you would do with the MLE). Thus, even though the credible sets don't have coverage, you can still produce valid confidence ... | Why should I be Bayesian when my model is wrong? | Here are a few other ways of justifying Bayesian inference in misspecified models.
You can construct a confidence interval on the posterior mean, using the sandwich formula (in the same way that you | Why should I be Bayesian when my model is wrong?
Here are a few other ways of justifying Bayesian inference in misspecified models.
You can construct a confidence interval on the posterior mean, using the sandwich formula (in the same way that you would do with the MLE). Thus, even though the credible sets don't have ... | Why should I be Bayesian when my model is wrong?
Here are a few other ways of justifying Bayesian inference in misspecified models.
You can construct a confidence interval on the posterior mean, using the sandwich formula (in the same way that you |
2,319 | Why should I be Bayesian when my model is wrong? | There is the usual bias-variance tradeoff. Bayesian inference assuming M-closed case [1,2], has a smaller variance [3] but in the case of model misspecification the bias grows faster [4]. It is also possible to do Bayesian inference assuming M-open case [1,2], which has a higher variance [3] but in the case of model mi... | Why should I be Bayesian when my model is wrong? | There is the usual bias-variance tradeoff. Bayesian inference assuming M-closed case [1,2], has a smaller variance [3] but in the case of model misspecification the bias grows faster [4]. It is also p | Why should I be Bayesian when my model is wrong?
There is the usual bias-variance tradeoff. Bayesian inference assuming M-closed case [1,2], has a smaller variance [3] but in the case of model misspecification the bias grows faster [4]. It is also possible to do Bayesian inference assuming M-open case [1,2], which has ... | Why should I be Bayesian when my model is wrong?
There is the usual bias-variance tradeoff. Bayesian inference assuming M-closed case [1,2], has a smaller variance [3] but in the case of model misspecification the bias grows faster [4]. It is also p |
2,320 | Why should I be Bayesian when my model is wrong? | The MLE is still an estimator for a parameter in a model you specify and assume to be correct. The regression coefficients in a frequentist OLS can be estimated with the MLE and all the properties you want to attach to it (unbiased, a specific asymptotic variance) still assume your very specific linear model is correct... | Why should I be Bayesian when my model is wrong? | The MLE is still an estimator for a parameter in a model you specify and assume to be correct. The regression coefficients in a frequentist OLS can be estimated with the MLE and all the properties you | Why should I be Bayesian when my model is wrong?
The MLE is still an estimator for a parameter in a model you specify and assume to be correct. The regression coefficients in a frequentist OLS can be estimated with the MLE and all the properties you want to attach to it (unbiased, a specific asymptotic variance) still ... | Why should I be Bayesian when my model is wrong?
The MLE is still an estimator for a parameter in a model you specify and assume to be correct. The regression coefficients in a frequentist OLS can be estimated with the MLE and all the properties you |
2,321 | Why should I be Bayesian when my model is wrong? | assume that the real model of the data $p_{true}(X)$ differs from $p(X|\theta)$ for all values of $\theta$
Bayesian interpretation of this assumption is that there is an additional random variable $\phi$ and a value $\phi_0$ in its range $\phi_0$ such that $\int p(X|\theta,\phi=\phi_0) \mathrm{d}\theta =0$. Your prior... | Why should I be Bayesian when my model is wrong? | assume that the real model of the data $p_{true}(X)$ differs from $p(X|\theta)$ for all values of $\theta$
Bayesian interpretation of this assumption is that there is an additional random variable $\ | Why should I be Bayesian when my model is wrong?
assume that the real model of the data $p_{true}(X)$ differs from $p(X|\theta)$ for all values of $\theta$
Bayesian interpretation of this assumption is that there is an additional random variable $\phi$ and a value $\phi_0$ in its range $\phi_0$ such that $\int p(X|\th... | Why should I be Bayesian when my model is wrong?
assume that the real model of the data $p_{true}(X)$ differs from $p(X|\theta)$ for all values of $\theta$
Bayesian interpretation of this assumption is that there is an additional random variable $\ |
2,322 | Why should I be Bayesian when my model is wrong? | I recommend Gelman & Shalizi's Philosophy and the practice of Bayesian statistics. They have coherent, detailed and practical responses to these questions.
We think most of this received view of Bayesian inference is wrong. Bayesian methods are no more inductive than any other mode of statistical inference. Bayesian d... | Why should I be Bayesian when my model is wrong? | I recommend Gelman & Shalizi's Philosophy and the practice of Bayesian statistics. They have coherent, detailed and practical responses to these questions.
We think most of this received view of Baye | Why should I be Bayesian when my model is wrong?
I recommend Gelman & Shalizi's Philosophy and the practice of Bayesian statistics. They have coherent, detailed and practical responses to these questions.
We think most of this received view of Bayesian inference is wrong. Bayesian methods are no more inductive than an... | Why should I be Bayesian when my model is wrong?
I recommend Gelman & Shalizi's Philosophy and the practice of Bayesian statistics. They have coherent, detailed and practical responses to these questions.
We think most of this received view of Baye |
2,323 | Why should I be Bayesian when my model is wrong? | I think you're describing an impact of model uncertainty - you worry that your inference about an unknown parameter $x$ in light of data $d$ is conditional upon a model, $m$,
$$
p (x|d, m),
$$
as well as the data. What if $m$ is an implausible model? If there exist alternative models, with the same unknown parameter $x... | Why should I be Bayesian when my model is wrong? | I think you're describing an impact of model uncertainty - you worry that your inference about an unknown parameter $x$ in light of data $d$ is conditional upon a model, $m$,
$$
p (x|d, m),
$$
as well | Why should I be Bayesian when my model is wrong?
I think you're describing an impact of model uncertainty - you worry that your inference about an unknown parameter $x$ in light of data $d$ is conditional upon a model, $m$,
$$
p (x|d, m),
$$
as well as the data. What if $m$ is an implausible model? If there exist alter... | Why should I be Bayesian when my model is wrong?
I think you're describing an impact of model uncertainty - you worry that your inference about an unknown parameter $x$ in light of data $d$ is conditional upon a model, $m$,
$$
p (x|d, m),
$$
as well |
2,324 | Why should I be Bayesian when my model is wrong? | How do you define what a "mis-specified" model is? Does this mean the model...
makes "bad" predictions?
is not of the form $p_{T}(x) $ for some "true model"?
is missing a parameter?
leads to "bad" conclusions?
If you think of the ways a given model could be mis-specified, you will essentially be extracting informat... | Why should I be Bayesian when my model is wrong? | How do you define what a "mis-specified" model is? Does this mean the model...
makes "bad" predictions?
is not of the form $p_{T}(x) $ for some "true model"?
is missing a parameter?
leads to "bad" | Why should I be Bayesian when my model is wrong?
How do you define what a "mis-specified" model is? Does this mean the model...
makes "bad" predictions?
is not of the form $p_{T}(x) $ for some "true model"?
is missing a parameter?
leads to "bad" conclusions?
If you think of the ways a given model could be mis-speci... | Why should I be Bayesian when my model is wrong?
How do you define what a "mis-specified" model is? Does this mean the model...
makes "bad" predictions?
is not of the form $p_{T}(x) $ for some "true model"?
is missing a parameter?
leads to "bad" |
2,325 | An example: LASSO regression using glmnet for binary outcome | library(glmnet)
age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7)
gender <- as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0))
bmi_p <- c(0.86, 0.45, 0.99, 0.84, 0.85, 0.67, 0.91, 0.29, 0.88)
m_edu <- as.factor(c(0, 1, 1, 2, 2, 3, 2, 0, 1))
p_edu <- as.factor(c(0, 2, 2, 2, 2, 3, 2, 0, 0))
f_color <- as.factor(c("blue", "blue",... | An example: LASSO regression using glmnet for binary outcome | library(glmnet)
age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7)
gender <- as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0))
bmi_p <- c(0.86, 0.45, 0.99, 0.84, 0.85, 0.67, 0.91, 0.29, 0.88)
m_edu <- as.factor | An example: LASSO regression using glmnet for binary outcome
library(glmnet)
age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7)
gender <- as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0))
bmi_p <- c(0.86, 0.45, 0.99, 0.84, 0.85, 0.67, 0.91, 0.29, 0.88)
m_edu <- as.factor(c(0, 1, 1, 2, 2, 3, 2, 0, 1))
p_edu <- as.factor(c(0, 2,... | An example: LASSO regression using glmnet for binary outcome
library(glmnet)
age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7)
gender <- as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0))
bmi_p <- c(0.86, 0.45, 0.99, 0.84, 0.85, 0.67, 0.91, 0.29, 0.88)
m_edu <- as.factor |
2,326 | An example: LASSO regression using glmnet for binary outcome | I will use the package enet since that is my preffered method. It is a little more flexible.
install.packages('elasticnet')
library(elasticnet)
age <- c(4,8,7,12,6,9,10,14,7)
gender <- c(1,0,1,1,1,0,1,0,0)
bmi_p <- c(0.86,0.45,0.99,0.84,0.85,0.67,0.91,0.29,0.88)
m_edu <- c(0,1,1,2,2,3,2,0,1)
p_edu <- c(0,2,2,2,2,3,2... | An example: LASSO regression using glmnet for binary outcome | I will use the package enet since that is my preffered method. It is a little more flexible.
install.packages('elasticnet')
library(elasticnet)
age <- c(4,8,7,12,6,9,10,14,7)
gender <- c(1,0,1,1,1, | An example: LASSO regression using glmnet for binary outcome
I will use the package enet since that is my preffered method. It is a little more flexible.
install.packages('elasticnet')
library(elasticnet)
age <- c(4,8,7,12,6,9,10,14,7)
gender <- c(1,0,1,1,1,0,1,0,0)
bmi_p <- c(0.86,0.45,0.99,0.84,0.85,0.67,0.91,0.29... | An example: LASSO regression using glmnet for binary outcome
I will use the package enet since that is my preffered method. It is a little more flexible.
install.packages('elasticnet')
library(elasticnet)
age <- c(4,8,7,12,6,9,10,14,7)
gender <- c(1,0,1,1,1, |
2,327 | An example: LASSO regression using glmnet for binary outcome | Just to expand on the excellent example provided by pat. The original problem posed ordinal variables (m_edu, p_edu), with an inherent order between levels (0 < 1 < 2 < 3). In pat's original answer I think these were treated as nominal categorical variables with no order between them. I may be wrong, but I believe thes... | An example: LASSO regression using glmnet for binary outcome | Just to expand on the excellent example provided by pat. The original problem posed ordinal variables (m_edu, p_edu), with an inherent order between levels (0 < 1 < 2 < 3). In pat's original answer I | An example: LASSO regression using glmnet for binary outcome
Just to expand on the excellent example provided by pat. The original problem posed ordinal variables (m_edu, p_edu), with an inherent order between levels (0 < 1 < 2 < 3). In pat's original answer I think these were treated as nominal categorical variables w... | An example: LASSO regression using glmnet for binary outcome
Just to expand on the excellent example provided by pat. The original problem posed ordinal variables (m_edu, p_edu), with an inherent order between levels (0 < 1 < 2 < 3). In pat's original answer I |
2,328 | What are the shortcomings of the Mean Absolute Percentage Error (MAPE)? | Shortcomings of the MAPE
The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense. It doesn't make sense to calculate percentages of temperatures, for instance, so you shouldn't use the MAPE to calculate the accuracy of a temperature forecast.
If just a single actual is zero, $A_t=... | What are the shortcomings of the Mean Absolute Percentage Error (MAPE)? | Shortcomings of the MAPE
The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense. It doesn't make sense to calculate percentages of temperatures, for instance, so | What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?
Shortcomings of the MAPE
The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense. It doesn't make sense to calculate percentages of temperatures, for instance, so you shouldn't use the MAPE to calculate the acc... | What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?
Shortcomings of the MAPE
The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense. It doesn't make sense to calculate percentages of temperatures, for instance, so |
2,329 | What are modern, easily used alternatives to stepwise regression? | There are several alternatives to Stepwise Regression. The most used I have seen are:
Expert opinion to decide which variables to include in the model.
Partial Least Squares Regression. You essentially get latent variables and do a regression with them. You could also do PCA yourself and then use the principal variabl... | What are modern, easily used alternatives to stepwise regression? | There are several alternatives to Stepwise Regression. The most used I have seen are:
Expert opinion to decide which variables to include in the model.
Partial Least Squares Regression. You essential | What are modern, easily used alternatives to stepwise regression?
There are several alternatives to Stepwise Regression. The most used I have seen are:
Expert opinion to decide which variables to include in the model.
Partial Least Squares Regression. You essentially get latent variables and do a regression with them.... | What are modern, easily used alternatives to stepwise regression?
There are several alternatives to Stepwise Regression. The most used I have seen are:
Expert opinion to decide which variables to include in the model.
Partial Least Squares Regression. You essential |
2,330 | What are modern, easily used alternatives to stepwise regression? | Another option you might consider for variable selection and regularization is the elastic net. It's implemented in R via the glmnet package. | What are modern, easily used alternatives to stepwise regression? | Another option you might consider for variable selection and regularization is the elastic net. It's implemented in R via the glmnet package. | What are modern, easily used alternatives to stepwise regression?
Another option you might consider for variable selection and regularization is the elastic net. It's implemented in R via the glmnet package. | What are modern, easily used alternatives to stepwise regression?
Another option you might consider for variable selection and regularization is the elastic net. It's implemented in R via the glmnet package. |
2,331 | What are modern, easily used alternatives to stepwise regression? | Model averaging is one way to go (an information-theoretic approach). The R package glmulti can perform linear models for every combination of predictor variables, and perform model averaging for these results.
See http://sites.google.com/site/mcgillbgsa/workshops/glmulti
Don't forget to investigate collinearity betw... | What are modern, easily used alternatives to stepwise regression? | Model averaging is one way to go (an information-theoretic approach). The R package glmulti can perform linear models for every combination of predictor variables, and perform model averaging for thes | What are modern, easily used alternatives to stepwise regression?
Model averaging is one way to go (an information-theoretic approach). The R package glmulti can perform linear models for every combination of predictor variables, and perform model averaging for these results.
See http://sites.google.com/site/mcgillbgs... | What are modern, easily used alternatives to stepwise regression?
Model averaging is one way to go (an information-theoretic approach). The R package glmulti can perform linear models for every combination of predictor variables, and perform model averaging for thes |
2,332 | What are modern, easily used alternatives to stepwise regression? | Interesting discussion. To label stepwise regression as statistical sin is a bit of a religious statement - as long as one knows what they are doing and that the objectives of the exercise is clear, it is definitely a fine approach with its own set of assumptions and, is certainly biased, and does not guarantee optimal... | What are modern, easily used alternatives to stepwise regression? | Interesting discussion. To label stepwise regression as statistical sin is a bit of a religious statement - as long as one knows what they are doing and that the objectives of the exercise is clear, i | What are modern, easily used alternatives to stepwise regression?
Interesting discussion. To label stepwise regression as statistical sin is a bit of a religious statement - as long as one knows what they are doing and that the objectives of the exercise is clear, it is definitely a fine approach with its own set of as... | What are modern, easily used alternatives to stepwise regression?
Interesting discussion. To label stepwise regression as statistical sin is a bit of a religious statement - as long as one knows what they are doing and that the objectives of the exercise is clear, i |
2,333 | What are modern, easily used alternatives to stepwise regression? | @johannes gave an excellent answer. If you are a SAS user, then LASSO is available through PROC GLMSELECT and partial least squares through PROC PLS.
David Cassell and I made a presentation about LASSO (and Least Angle Regression) at a couple of SAS user groups. It's available here | What are modern, easily used alternatives to stepwise regression? | @johannes gave an excellent answer. If you are a SAS user, then LASSO is available through PROC GLMSELECT and partial least squares through PROC PLS.
David Cassell and I made a presentation about LASS | What are modern, easily used alternatives to stepwise regression?
@johannes gave an excellent answer. If you are a SAS user, then LASSO is available through PROC GLMSELECT and partial least squares through PROC PLS.
David Cassell and I made a presentation about LASSO (and Least Angle Regression) at a couple of SAS user... | What are modern, easily used alternatives to stepwise regression?
@johannes gave an excellent answer. If you are a SAS user, then LASSO is available through PROC GLMSELECT and partial least squares through PROC PLS.
David Cassell and I made a presentation about LASS |
2,334 | Why not approach classification through regression? | "..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of fitting a logistic regression model.
Before we do this, it is important to clarify the distinction between regression and classificatio... | Why not approach classification through regression? | "..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of fitting a logist | Why not approach classification through regression?
"..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of fitting a logistic regression model.
Before we do this, it is important to clarify ... | Why not approach classification through regression?
"..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of fitting a logist |
2,335 | Why not approach classification through regression? | I can't think of an example in which classification is actually the ultimate goal. Almost always the real goal is to make accurate predictions, e.g., of probabilities. In that spirit, (logistic) regression is your friend. | Why not approach classification through regression? | I can't think of an example in which classification is actually the ultimate goal. Almost always the real goal is to make accurate predictions, e.g., of probabilities. In that spirit, (logistic) reg | Why not approach classification through regression?
I can't think of an example in which classification is actually the ultimate goal. Almost always the real goal is to make accurate predictions, e.g., of probabilities. In that spirit, (logistic) regression is your friend. | Why not approach classification through regression?
I can't think of an example in which classification is actually the ultimate goal. Almost always the real goal is to make accurate predictions, e.g., of probabilities. In that spirit, (logistic) reg |
2,336 | Why not approach classification through regression? | Why not look at some evidence? Although many would argue that linear regression is not right for classification, it may still work. To gain some intuition, I included linear regression (used as classifier) into scikit-learn's classifier comparison. Here is what happens:
The decision boundary is narrower than with the ... | Why not approach classification through regression? | Why not look at some evidence? Although many would argue that linear regression is not right for classification, it may still work. To gain some intuition, I included linear regression (used as classi | Why not approach classification through regression?
Why not look at some evidence? Although many would argue that linear regression is not right for classification, it may still work. To gain some intuition, I included linear regression (used as classifier) into scikit-learn's classifier comparison. Here is what happen... | Why not approach classification through regression?
Why not look at some evidence? Although many would argue that linear regression is not right for classification, it may still work. To gain some intuition, I included linear regression (used as classi |
2,337 | Why not approach classification through regression? | Further, to expand on already good answers, for any classification task beyond a bivariate one, using the regression would require us to impose a distance and ordering between the classes. In other words, we might get different results just by shuffling the labels of the classes or changing the scale of assigned numeri... | Why not approach classification through regression? | Further, to expand on already good answers, for any classification task beyond a bivariate one, using the regression would require us to impose a distance and ordering between the classes. In other wo | Why not approach classification through regression?
Further, to expand on already good answers, for any classification task beyond a bivariate one, using the regression would require us to impose a distance and ordering between the classes. In other words, we might get different results just by shuffling the labels of ... | Why not approach classification through regression?
Further, to expand on already good answers, for any classification task beyond a bivariate one, using the regression would require us to impose a distance and ordering between the classes. In other wo |
2,338 | How to produce a pretty plot of the results of k-means cluster analysis? | I'd push the silhouette plot for this, because it's unlikely that you'll get much actionable information from pair plots when the number of dimension is 14.
library(cluster)
library(HSAUR)
data(pottery)
km <- kmeans(pottery,3)
dissE <- daisy(pottery)
dE2 <- dissE^2
sk2 <- silhouette(km$cl, dE2)
plot(sk2)
This ... | How to produce a pretty plot of the results of k-means cluster analysis? | I'd push the silhouette plot for this, because it's unlikely that you'll get much actionable information from pair plots when the number of dimension is 14.
library(cluster)
library(HSAUR)
data(potter | How to produce a pretty plot of the results of k-means cluster analysis?
I'd push the silhouette plot for this, because it's unlikely that you'll get much actionable information from pair plots when the number of dimension is 14.
library(cluster)
library(HSAUR)
data(pottery)
km <- kmeans(pottery,3)
dissE <- daisy(po... | How to produce a pretty plot of the results of k-means cluster analysis?
I'd push the silhouette plot for this, because it's unlikely that you'll get much actionable information from pair plots when the number of dimension is 14.
library(cluster)
library(HSAUR)
data(potter |
2,339 | How to produce a pretty plot of the results of k-means cluster analysis? | Here an example that can helps you:
library(cluster)
library(fpc)
data(iris)
dat <- iris[, -5] # without known classification
# Kmeans clustre analysis
clus <- kmeans(dat, centers=3)
# Fig 01
plotcluster(dat, clus$cluster)
# More complex
clusplot(dat, clus$cluster, color=TRUE, shade=TRUE,
labels=2, l... | How to produce a pretty plot of the results of k-means cluster analysis? | Here an example that can helps you:
library(cluster)
library(fpc)
data(iris)
dat <- iris[, -5] # without known classification
# Kmeans clustre analysis
clus <- kmeans(dat, centers=3)
# Fig 01
p | How to produce a pretty plot of the results of k-means cluster analysis?
Here an example that can helps you:
library(cluster)
library(fpc)
data(iris)
dat <- iris[, -5] # without known classification
# Kmeans clustre analysis
clus <- kmeans(dat, centers=3)
# Fig 01
plotcluster(dat, clus$cluster)
# More complex
... | How to produce a pretty plot of the results of k-means cluster analysis?
Here an example that can helps you:
library(cluster)
library(fpc)
data(iris)
dat <- iris[, -5] # without known classification
# Kmeans clustre analysis
clus <- kmeans(dat, centers=3)
# Fig 01
p |
2,340 | How to produce a pretty plot of the results of k-means cluster analysis? | The simplest way I know to do that is the following:
X <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))
km <- kmeans(X, center=2)
plot(X,col=km$cluster)
points(km$center,col=1:2,pch=8,cex=1)
In this way you can draw the points of each cluster using a different color and their centroids. | How to produce a pretty plot of the results of k-means cluster analysis? | The simplest way I know to do that is the following:
X <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))
km <- kmeans(X, center=2)
plot(X,col=km$cluster)
points(km$center,col=1:2,pch=8,cex=1) | How to produce a pretty plot of the results of k-means cluster analysis?
The simplest way I know to do that is the following:
X <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))
km <- kmeans(X, center=2)
plot(X,col=km$cluster)
points(km$center,col=1:2,pch=8,cex=1)
In this way you can draw the points of each c... | How to produce a pretty plot of the results of k-means cluster analysis?
The simplest way I know to do that is the following:
X <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))
km <- kmeans(X, center=2)
plot(X,col=km$cluster)
points(km$center,col=1:2,pch=8,cex=1) |
2,341 | How to produce a pretty plot of the results of k-means cluster analysis? | This is an old question at this point, but I think the factoextra package has several useful tools for clustering and plots. For example, the fviz_cluster() function, which plots PCA dimensions 1 and 2 in a scatter plot and colors and groups the clusters. This demo goes through some different functions from factoextr... | How to produce a pretty plot of the results of k-means cluster analysis? | This is an old question at this point, but I think the factoextra package has several useful tools for clustering and plots. For example, the fviz_cluster() function, which plots PCA dimensions 1 and | How to produce a pretty plot of the results of k-means cluster analysis?
This is an old question at this point, but I think the factoextra package has several useful tools for clustering and plots. For example, the fviz_cluster() function, which plots PCA dimensions 1 and 2 in a scatter plot and colors and groups the ... | How to produce a pretty plot of the results of k-means cluster analysis?
This is an old question at this point, but I think the factoextra package has several useful tools for clustering and plots. For example, the fviz_cluster() function, which plots PCA dimensions 1 and |
2,342 | Rules of thumb for "modern" statistics | Don't forget to do some basic data checking before you start the analysis. In particular, look at a scatter plot of every variable you intend to analyse against ID number, date / time of data collection or similar. The eye can often pick up patterns that reveal problems when summary statistics don't show anything unusu... | Rules of thumb for "modern" statistics | Don't forget to do some basic data checking before you start the analysis. In particular, look at a scatter plot of every variable you intend to analyse against ID number, date / time of data collecti | Rules of thumb for "modern" statistics
Don't forget to do some basic data checking before you start the analysis. In particular, look at a scatter plot of every variable you intend to analyse against ID number, date / time of data collection or similar. The eye can often pick up patterns that reveal problems when summa... | Rules of thumb for "modern" statistics
Don't forget to do some basic data checking before you start the analysis. In particular, look at a scatter plot of every variable you intend to analyse against ID number, date / time of data collecti |
2,343 | Rules of thumb for "modern" statistics | Keep your analysis reproducible. A reviewer or your boss or someone else will eventually ask you how exactly you arrived at your result - probably six months or more after you did the analysis. You will not remember how you cleaned the data, what analysis you did, why you chose the specific model you used... And recons... | Rules of thumb for "modern" statistics | Keep your analysis reproducible. A reviewer or your boss or someone else will eventually ask you how exactly you arrived at your result - probably six months or more after you did the analysis. You wi | Rules of thumb for "modern" statistics
Keep your analysis reproducible. A reviewer or your boss or someone else will eventually ask you how exactly you arrived at your result - probably six months or more after you did the analysis. You will not remember how you cleaned the data, what analysis you did, why you chose th... | Rules of thumb for "modern" statistics
Keep your analysis reproducible. A reviewer or your boss or someone else will eventually ask you how exactly you arrived at your result - probably six months or more after you did the analysis. You wi |
2,344 | Rules of thumb for "modern" statistics | There is no free lunch
A large part of statistical failures is created by clicking a big shiny button called "Calculate significance" without taking into account its burden of hidden assumptions.
Repeat
Even if a single call to a random generator is involved, one may have luck or bad luck and so jump to the wrong concl... | Rules of thumb for "modern" statistics | There is no free lunch
A large part of statistical failures is created by clicking a big shiny button called "Calculate significance" without taking into account its burden of hidden assumptions.
Repe | Rules of thumb for "modern" statistics
There is no free lunch
A large part of statistical failures is created by clicking a big shiny button called "Calculate significance" without taking into account its burden of hidden assumptions.
Repeat
Even if a single call to a random generator is involved, one may have luck or ... | Rules of thumb for "modern" statistics
There is no free lunch
A large part of statistical failures is created by clicking a big shiny button called "Calculate significance" without taking into account its burden of hidden assumptions.
Repe |
2,345 | Rules of thumb for "modern" statistics | One rule per answer ;-)
Talk to the statistician before conducting the study. If possible, before applying for the grant. Help him/her understand the problem you are studying, get his/her input on how to analyze the data you are about to collect and think about what that means for your study design and data requirement... | Rules of thumb for "modern" statistics | One rule per answer ;-)
Talk to the statistician before conducting the study. If possible, before applying for the grant. Help him/her understand the problem you are studying, get his/her input on how | Rules of thumb for "modern" statistics
One rule per answer ;-)
Talk to the statistician before conducting the study. If possible, before applying for the grant. Help him/her understand the problem you are studying, get his/her input on how to analyze the data you are about to collect and think about what that means for... | Rules of thumb for "modern" statistics
One rule per answer ;-)
Talk to the statistician before conducting the study. If possible, before applying for the grant. Help him/her understand the problem you are studying, get his/her input on how |
2,346 | Rules of thumb for "modern" statistics | One thing I tell my students is to produce an appropriate graph for every p-value. e.g., a scatterplot if they test correlation, side-by-side boxplots if they do a one-way ANOVA, etc. | Rules of thumb for "modern" statistics | One thing I tell my students is to produce an appropriate graph for every p-value. e.g., a scatterplot if they test correlation, side-by-side boxplots if they do a one-way ANOVA, etc. | Rules of thumb for "modern" statistics
One thing I tell my students is to produce an appropriate graph for every p-value. e.g., a scatterplot if they test correlation, side-by-side boxplots if they do a one-way ANOVA, etc. | Rules of thumb for "modern" statistics
One thing I tell my students is to produce an appropriate graph for every p-value. e.g., a scatterplot if they test correlation, side-by-side boxplots if they do a one-way ANOVA, etc. |
2,347 | Rules of thumb for "modern" statistics | If you're deciding between two ways of analysing your data, try it both ways and see if it makes a difference.
This is useful in many contexts:
To transform or not transform
Non-parametric or parameteric test
Spearman's or Pearson's correlation
PCA or factor analysis
Whether to use the arithmetic mean or a robust est... | Rules of thumb for "modern" statistics | If you're deciding between two ways of analysing your data, try it both ways and see if it makes a difference.
This is useful in many contexts:
To transform or not transform
Non-parametric or parame | Rules of thumb for "modern" statistics
If you're deciding between two ways of analysing your data, try it both ways and see if it makes a difference.
This is useful in many contexts:
To transform or not transform
Non-parametric or parameteric test
Spearman's or Pearson's correlation
PCA or factor analysis
Whether to ... | Rules of thumb for "modern" statistics
If you're deciding between two ways of analysing your data, try it both ways and see if it makes a difference.
This is useful in many contexts:
To transform or not transform
Non-parametric or parame |
2,348 | Rules of thumb for "modern" statistics | Question your data. In the modern era of cheap RAM, we often work on large amounts of data. One 'fat-finger' error or 'lost decimal place' can easily dominate an analysis. Without some basic sanity checking, (or plotting the data, as suggested by others here) one can waste a lot of time. This also suggests using some b... | Rules of thumb for "modern" statistics | Question your data. In the modern era of cheap RAM, we often work on large amounts of data. One 'fat-finger' error or 'lost decimal place' can easily dominate an analysis. Without some basic sanity ch | Rules of thumb for "modern" statistics
Question your data. In the modern era of cheap RAM, we often work on large amounts of data. One 'fat-finger' error or 'lost decimal place' can easily dominate an analysis. Without some basic sanity checking, (or plotting the data, as suggested by others here) one can waste a lot o... | Rules of thumb for "modern" statistics
Question your data. In the modern era of cheap RAM, we often work on large amounts of data. One 'fat-finger' error or 'lost decimal place' can easily dominate an analysis. Without some basic sanity ch |
2,349 | Rules of thumb for "modern" statistics | Use software that shows the chain of programming logic from the raw data through to the final analyses/results. Avoid software like Excel where one user can make an undetectable error in one cell, that only manual checking will pick up. | Rules of thumb for "modern" statistics | Use software that shows the chain of programming logic from the raw data through to the final analyses/results. Avoid software like Excel where one user can make an undetectable error in one cell, tha | Rules of thumb for "modern" statistics
Use software that shows the chain of programming logic from the raw data through to the final analyses/results. Avoid software like Excel where one user can make an undetectable error in one cell, that only manual checking will pick up. | Rules of thumb for "modern" statistics
Use software that shows the chain of programming logic from the raw data through to the final analyses/results. Avoid software like Excel where one user can make an undetectable error in one cell, tha |
2,350 | Rules of thumb for "modern" statistics | Always ask yourself "what do these results mean and how will they be used?"
Usually the purpose of using statistics is to assist in making decisions under uncertainty. So it is important to have at the front of your mind "What decisions will be made as a result of this analysis and how will this analysis influence the... | Rules of thumb for "modern" statistics | Always ask yourself "what do these results mean and how will they be used?"
Usually the purpose of using statistics is to assist in making decisions under uncertainty. So it is important to have at t | Rules of thumb for "modern" statistics
Always ask yourself "what do these results mean and how will they be used?"
Usually the purpose of using statistics is to assist in making decisions under uncertainty. So it is important to have at the front of your mind "What decisions will be made as a result of this analysis a... | Rules of thumb for "modern" statistics
Always ask yourself "what do these results mean and how will they be used?"
Usually the purpose of using statistics is to assist in making decisions under uncertainty. So it is important to have at t |
2,351 | Rules of thumb for "modern" statistics | There can be a long list but to mention a few: (in no specific order)
P-value is NOT probability. Specifically, it is not the probability of committing Type I error. Similarly, CIs have no probabilistic interpretation for the given data. They are applicable for repeated experiments.
Problem related to variance dominat... | Rules of thumb for "modern" statistics | There can be a long list but to mention a few: (in no specific order)
P-value is NOT probability. Specifically, it is not the probability of committing Type I error. Similarly, CIs have no probabilis | Rules of thumb for "modern" statistics
There can be a long list but to mention a few: (in no specific order)
P-value is NOT probability. Specifically, it is not the probability of committing Type I error. Similarly, CIs have no probabilistic interpretation for the given data. They are applicable for repeated experimen... | Rules of thumb for "modern" statistics
There can be a long list but to mention a few: (in no specific order)
P-value is NOT probability. Specifically, it is not the probability of committing Type I error. Similarly, CIs have no probabilis |
2,352 | Rules of thumb for "modern" statistics | For data organization/management, ensure that when you generate new variables in the dataset (for example, calculating body mass index from height and weight), the original variables are never deleted. A non-destructive approach is best from a reproducibility perspective. You never know when you might mis-enter a comma... | Rules of thumb for "modern" statistics | For data organization/management, ensure that when you generate new variables in the dataset (for example, calculating body mass index from height and weight), the original variables are never deleted | Rules of thumb for "modern" statistics
For data organization/management, ensure that when you generate new variables in the dataset (for example, calculating body mass index from height and weight), the original variables are never deleted. A non-destructive approach is best from a reproducibility perspective. You neve... | Rules of thumb for "modern" statistics
For data organization/management, ensure that when you generate new variables in the dataset (for example, calculating body mass index from height and weight), the original variables are never deleted |
2,353 | Rules of thumb for "modern" statistics | Think hard about the underlying data generating process (DGP). If the model you want to use doesn't reflect the DGP, you need to find a new model. | Rules of thumb for "modern" statistics | Think hard about the underlying data generating process (DGP). If the model you want to use doesn't reflect the DGP, you need to find a new model. | Rules of thumb for "modern" statistics
Think hard about the underlying data generating process (DGP). If the model you want to use doesn't reflect the DGP, you need to find a new model. | Rules of thumb for "modern" statistics
Think hard about the underlying data generating process (DGP). If the model you want to use doesn't reflect the DGP, you need to find a new model. |
2,354 | Rules of thumb for "modern" statistics | For histograms, a good rule of thumb for number of bins in a histogram:
square root of the number of data points | Rules of thumb for "modern" statistics | For histograms, a good rule of thumb for number of bins in a histogram:
square root of the number of data points | Rules of thumb for "modern" statistics
For histograms, a good rule of thumb for number of bins in a histogram:
square root of the number of data points | Rules of thumb for "modern" statistics
For histograms, a good rule of thumb for number of bins in a histogram:
square root of the number of data points |
2,355 | Rules of thumb for "modern" statistics | Despite increasingly larger datasets and more powerful software, over-fitting models is a major danger to researchers, especially those who have not yet been burned by over-fitting. Over-fitting means that you have fitted something more complicated than your data and the state of the art. Like love or beauty, it is har... | Rules of thumb for "modern" statistics | Despite increasingly larger datasets and more powerful software, over-fitting models is a major danger to researchers, especially those who have not yet been burned by over-fitting. Over-fitting means | Rules of thumb for "modern" statistics
Despite increasingly larger datasets and more powerful software, over-fitting models is a major danger to researchers, especially those who have not yet been burned by over-fitting. Over-fitting means that you have fitted something more complicated than your data and the state of ... | Rules of thumb for "modern" statistics
Despite increasingly larger datasets and more powerful software, over-fitting models is a major danger to researchers, especially those who have not yet been burned by over-fitting. Over-fitting means |
2,356 | Rules of thumb for "modern" statistics | In a forecasting problem (i.e., when you need to forecast $Y_{t+h}$ given $(Y_t,X_t)$ $t>T$, with the use of a learning set $(Y_1,X_1),\dots, (Y_T,X_T)$ ), the rule of the thumb (to be done before any complex modelling) are
Climatology ($Y_{t+h}$ forecast by the mean observed value over the learning set, possibly by r... | Rules of thumb for "modern" statistics | In a forecasting problem (i.e., when you need to forecast $Y_{t+h}$ given $(Y_t,X_t)$ $t>T$, with the use of a learning set $(Y_1,X_1),\dots, (Y_T,X_T)$ ), the rule of the thumb (to be done before any | Rules of thumb for "modern" statistics
In a forecasting problem (i.e., when you need to forecast $Y_{t+h}$ given $(Y_t,X_t)$ $t>T$, with the use of a learning set $(Y_1,X_1),\dots, (Y_T,X_T)$ ), the rule of the thumb (to be done before any complex modelling) are
Climatology ($Y_{t+h}$ forecast by the mean observed val... | Rules of thumb for "modern" statistics
In a forecasting problem (i.e., when you need to forecast $Y_{t+h}$ given $(Y_t,X_t)$ $t>T$, with the use of a learning set $(Y_1,X_1),\dots, (Y_T,X_T)$ ), the rule of the thumb (to be done before any |
2,357 | Rules of thumb for "modern" statistics | If the model won't converge easily and quickly, it could be the fault of the software. It is, however, much more common that your data are not suitable for the model or the model is not suitable for the data. It could be hard to tell which, and empiricists and theorists can have different views. But subject-matter thin... | Rules of thumb for "modern" statistics | If the model won't converge easily and quickly, it could be the fault of the software. It is, however, much more common that your data are not suitable for the model or the model is not suitable for t | Rules of thumb for "modern" statistics
If the model won't converge easily and quickly, it could be the fault of the software. It is, however, much more common that your data are not suitable for the model or the model is not suitable for the data. It could be hard to tell which, and empiricists and theorists can have d... | Rules of thumb for "modern" statistics
If the model won't converge easily and quickly, it could be the fault of the software. It is, however, much more common that your data are not suitable for the model or the model is not suitable for t |
2,358 | Rules of thumb for "modern" statistics | In instrumental variables regression always check the joint significance of your instruments. The Staiger-Stock rule of thumb says that an F-statistic of less than 10 is worrisome and indicates that your instruments might be weak, i.e. they are not sufficiently correlated with the endogenous variable. However, this doe... | Rules of thumb for "modern" statistics | In instrumental variables regression always check the joint significance of your instruments. The Staiger-Stock rule of thumb says that an F-statistic of less than 10 is worrisome and indicates that y | Rules of thumb for "modern" statistics
In instrumental variables regression always check the joint significance of your instruments. The Staiger-Stock rule of thumb says that an F-statistic of less than 10 is worrisome and indicates that your instruments might be weak, i.e. they are not sufficiently correlated with the... | Rules of thumb for "modern" statistics
In instrumental variables regression always check the joint significance of your instruments. The Staiger-Stock rule of thumb says that an F-statistic of less than 10 is worrisome and indicates that y |
2,359 | Rules of thumb for "modern" statistics | There are no criteria to choose information criteria.
Once someone says something like "The ?IC indicates this, but it is known often to give the wrong results" (where ? is any letter you like), you know that you will have also to think about the model and particularly whether it makes scientific or practical sense.
... | Rules of thumb for "modern" statistics | There are no criteria to choose information criteria.
Once someone says something like "The ?IC indicates this, but it is known often to give the wrong results" (where ? is any letter you like), you | Rules of thumb for "modern" statistics
There are no criteria to choose information criteria.
Once someone says something like "The ?IC indicates this, but it is known often to give the wrong results" (where ? is any letter you like), you know that you will have also to think about the model and particularly whether it... | Rules of thumb for "modern" statistics
There are no criteria to choose information criteria.
Once someone says something like "The ?IC indicates this, but it is known often to give the wrong results" (where ? is any letter you like), you |
2,360 | Rules of thumb for "modern" statistics | I read this somewhere (probably on cross validated) and I haven't been able to find it anywhere, so here goes...
If you've discovered an interesting result, it's probably wrong.
It's very easy to get excited by the prospect of a staggering p-value or a near perfect cross validation error. I've personally ecstatically p... | Rules of thumb for "modern" statistics | I read this somewhere (probably on cross validated) and I haven't been able to find it anywhere, so here goes...
If you've discovered an interesting result, it's probably wrong.
It's very easy to get | Rules of thumb for "modern" statistics
I read this somewhere (probably on cross validated) and I haven't been able to find it anywhere, so here goes...
If you've discovered an interesting result, it's probably wrong.
It's very easy to get excited by the prospect of a staggering p-value or a near perfect cross validatio... | Rules of thumb for "modern" statistics
I read this somewhere (probably on cross validated) and I haven't been able to find it anywhere, so here goes...
If you've discovered an interesting result, it's probably wrong.
It's very easy to get |
2,361 | Rules of thumb for "modern" statistics | Try to be valiant rather than virtuous That is, don't let petty signs of non-Normality, non-independence or non-linearity etc. block your road if such indications need to be disregarded in order to have the data speak loud and clear.
-- In Danish, 'dristig' vs. 'dydig' are the adjectives. | Rules of thumb for "modern" statistics | Try to be valiant rather than virtuous That is, don't let petty signs of non-Normality, non-independence or non-linearity etc. block your road if such indications need to be disregarded in order to ha | Rules of thumb for "modern" statistics
Try to be valiant rather than virtuous That is, don't let petty signs of non-Normality, non-independence or non-linearity etc. block your road if such indications need to be disregarded in order to have the data speak loud and clear.
-- In Danish, 'dristig' vs. 'dydig' are the adj... | Rules of thumb for "modern" statistics
Try to be valiant rather than virtuous That is, don't let petty signs of non-Normality, non-independence or non-linearity etc. block your road if such indications need to be disregarded in order to ha |
2,362 | Rules of thumb for "modern" statistics | When analyzing longitudinal data be sure to check that variables are coded the same way in each time period.
While writing my dissertation, which entailed analysis of secondary data, there was a week or so of utter bafflement of a 1-unit shift in mean depression scores across an otherwise stable mean by year: it turned... | Rules of thumb for "modern" statistics | When analyzing longitudinal data be sure to check that variables are coded the same way in each time period.
While writing my dissertation, which entailed analysis of secondary data, there was a week | Rules of thumb for "modern" statistics
When analyzing longitudinal data be sure to check that variables are coded the same way in each time period.
While writing my dissertation, which entailed analysis of secondary data, there was a week or so of utter bafflement of a 1-unit shift in mean depression scores across an o... | Rules of thumb for "modern" statistics
When analyzing longitudinal data be sure to check that variables are coded the same way in each time period.
While writing my dissertation, which entailed analysis of secondary data, there was a week |
2,363 | Rules of thumb for "modern" statistics | Your hypothesis should drive your choice of model, not the other way around.
To paraphrase Maslow, if you are a hammer, everything looks like a nail. Specific models come with blinders and assumptions about the world built right in: for example non-dynamic models choke on treatment-outcome feedback. | Rules of thumb for "modern" statistics | Your hypothesis should drive your choice of model, not the other way around.
To paraphrase Maslow, if you are a hammer, everything looks like a nail. Specific models come with blinders and assumptions | Rules of thumb for "modern" statistics
Your hypothesis should drive your choice of model, not the other way around.
To paraphrase Maslow, if you are a hammer, everything looks like a nail. Specific models come with blinders and assumptions about the world built right in: for example non-dynamic models choke on treatmen... | Rules of thumb for "modern" statistics
Your hypothesis should drive your choice of model, not the other way around.
To paraphrase Maslow, if you are a hammer, everything looks like a nail. Specific models come with blinders and assumptions |
2,364 | Rules of thumb for "modern" statistics | Use simulation to check where the structure of your model may be creating "results" which are simply mathematical artifacts of your model's assumptions
Perform your analysis on rerandomized variables, or on simulated variables known to be uncorrelated with one another. Do this many times and contrast averaged point est... | Rules of thumb for "modern" statistics | Use simulation to check where the structure of your model may be creating "results" which are simply mathematical artifacts of your model's assumptions
Perform your analysis on rerandomized variables, | Rules of thumb for "modern" statistics
Use simulation to check where the structure of your model may be creating "results" which are simply mathematical artifacts of your model's assumptions
Perform your analysis on rerandomized variables, or on simulated variables known to be uncorrelated with one another. Do this man... | Rules of thumb for "modern" statistics
Use simulation to check where the structure of your model may be creating "results" which are simply mathematical artifacts of your model's assumptions
Perform your analysis on rerandomized variables, |
2,365 | Rules of thumb for "modern" statistics | I am a data analyst rather than a statistician but these are my suggestions.
1)Before you analyze data make sure the assumptions of your method are right. Once you see results they can be hard to forget even after you fix the problems and the results change.
2) It helps to know your data. I run time series and got a re... | Rules of thumb for "modern" statistics | I am a data analyst rather than a statistician but these are my suggestions.
1)Before you analyze data make sure the assumptions of your method are right. Once you see results they can be hard to forg | Rules of thumb for "modern" statistics
I am a data analyst rather than a statistician but these are my suggestions.
1)Before you analyze data make sure the assumptions of your method are right. Once you see results they can be hard to forget even after you fix the problems and the results change.
2) It helps to know yo... | Rules of thumb for "modern" statistics
I am a data analyst rather than a statistician but these are my suggestions.
1)Before you analyze data make sure the assumptions of your method are right. Once you see results they can be hard to forg |
2,366 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | As Rob mentions, this occurs when you have highly correlated variables. The standard example I use is predicting weight from shoe size. You can predict weight equally well with the right or left shoe size. But together it doesn't work out.
Brief simulation example
RSS = 3:10 #Right shoe size
LSS = rnorm(RSS, RSS, 0.1) ... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | As Rob mentions, this occurs when you have highly correlated variables. The standard example I use is predicting weight from shoe size. You can predict weight equally well with the right or left shoe | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
As Rob mentions, this occurs when you have highly correlated variables. The standard example I use is predicting weight from shoe size. You can predict weight equally well with the right or left shoe size. But together it ... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
As Rob mentions, this occurs when you have highly correlated variables. The standard example I use is predicting weight from shoe size. You can predict weight equally well with the right or left shoe |
2,367 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | It takes very little correlation among the independent variables to cause this.
To see why, try the following:
Draw 50 sets of ten vectors $(x_1, x_2, \ldots, x_{10})$ with coefficients iid standard normal.
Compute $y_i = (x_i + x_{i+1})/\sqrt{2}$ for $i = 1, 2, \ldots, 9$. This makes the $y_i$ individually standard ... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | It takes very little correlation among the independent variables to cause this.
To see why, try the following:
Draw 50 sets of ten vectors $(x_1, x_2, \ldots, x_{10})$ with coefficients iid standard | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
It takes very little correlation among the independent variables to cause this.
To see why, try the following:
Draw 50 sets of ten vectors $(x_1, x_2, \ldots, x_{10})$ with coefficients iid standard normal.
Compute $y_i =... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
It takes very little correlation among the independent variables to cause this.
To see why, try the following:
Draw 50 sets of ten vectors $(x_1, x_2, \ldots, x_{10})$ with coefficients iid standard |
2,368 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | Multicollinearity
As you note, and as has been discussed in this previous question, high levels of multicollinearity is one major cause of a statistically significant $R^2$ but statically non-significant predictors.
Of course, multicollinearity is not just about an absolute threshold. Standard errors on regression co... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | Multicollinearity
As you note, and as has been discussed in this previous question, high levels of multicollinearity is one major cause of a statistically significant $R^2$ but statically non-signif | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
Multicollinearity
As you note, and as has been discussed in this previous question, high levels of multicollinearity is one major cause of a statistically significant $R^2$ but statically non-significant predictors.
Of c... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
Multicollinearity
As you note, and as has been discussed in this previous question, high levels of multicollinearity is one major cause of a statistically significant $R^2$ but statically non-signif |
2,369 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | This happens when the predictors are highly correlated. Imagine a situation where there are only two predictors with very high correlation. Individually, they both also correlate closely with the response variable. Consequently, the F-test has a low p-value (it is saying that the predictors together are highly signific... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | This happens when the predictors are highly correlated. Imagine a situation where there are only two predictors with very high correlation. Individually, they both also correlate closely with the resp | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
This happens when the predictors are highly correlated. Imagine a situation where there are only two predictors with very high correlation. Individually, they both also correlate closely with the response variable. Consequ... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
This happens when the predictors are highly correlated. Imagine a situation where there are only two predictors with very high correlation. Individually, they both also correlate closely with the resp |
2,370 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | Consider the following model: $ X_1 \sim N(0,1)$, $X_2 = a X_1 + \delta$, $Y = bX_1 + cX_2 + \epsilon$, $\delta$, $\epsilon$ and $X_1$ are all mutually independent $N(0,1)$.
Then $${\rm Cov}(X_2,Y) = {\rm E}[(aX_1+\delta)(bX_1+cX_2+\epsilon)]={\rm E}[(aX_1+\delta)(\{b+ac\}X_1+c\delta+\epsilon)]=a(b+ac)+c$$
We can set t... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | Consider the following model: $ X_1 \sim N(0,1)$, $X_2 = a X_1 + \delta$, $Y = bX_1 + cX_2 + \epsilon$, $\delta$, $\epsilon$ and $X_1$ are all mutually independent $N(0,1)$.
Then $${\rm Cov}(X_2,Y) = | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
Consider the following model: $ X_1 \sim N(0,1)$, $X_2 = a X_1 + \delta$, $Y = bX_1 + cX_2 + \epsilon$, $\delta$, $\epsilon$ and $X_1$ are all mutually independent $N(0,1)$.
Then $${\rm Cov}(X_2,Y) = {\rm E}[(aX_1+\delta)(... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
Consider the following model: $ X_1 \sim N(0,1)$, $X_2 = a X_1 + \delta$, $Y = bX_1 + cX_2 + \epsilon$, $\delta$, $\epsilon$ and $X_1$ are all mutually independent $N(0,1)$.
Then $${\rm Cov}(X_2,Y) = |
2,371 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | A keyword to search for would be "collinearity" or "multicollinearity". This can be detected using diagnostics like Variance Inflation Factors (VIFs) or methods as described inthe textbook "Regression Diagnostics: Identifying Influential Data and Sources of Collinearity" by Belsley, Kuh and Welsch. VIFs are much easier... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | A keyword to search for would be "collinearity" or "multicollinearity". This can be detected using diagnostics like Variance Inflation Factors (VIFs) or methods as described inthe textbook "Regression | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
A keyword to search for would be "collinearity" or "multicollinearity". This can be detected using diagnostics like Variance Inflation Factors (VIFs) or methods as described inthe textbook "Regression Diagnostics: Identify... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
A keyword to search for would be "collinearity" or "multicollinearity". This can be detected using diagnostics like Variance Inflation Factors (VIFs) or methods as described inthe textbook "Regression |
2,372 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | The answer you get depends on the question you ask. In addition to the points already made, the individual parameters F values and the overall model F values answer different questions, so they get different answers. I have seen this happen even when the individual F values are not that close to significant, especiall... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | The answer you get depends on the question you ask. In addition to the points already made, the individual parameters F values and the overall model F values answer different questions, so they get d | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
The answer you get depends on the question you ask. In addition to the points already made, the individual parameters F values and the overall model F values answer different questions, so they get different answers. I ha... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
The answer you get depends on the question you ask. In addition to the points already made, the individual parameters F values and the overall model F values answer different questions, so they get d |
2,373 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | One other thing to keep in mind is that the tests on the individual coefficients each assume that all of the other predictors are in the model. In other words each predictor is not significant as long as all of the other predictors are in the model. There must be some interaction or interdependence between two or more ... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | One other thing to keep in mind is that the tests on the individual coefficients each assume that all of the other predictors are in the model. In other words each predictor is not significant as long | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
One other thing to keep in mind is that the tests on the individual coefficients each assume that all of the other predictors are in the model. In other words each predictor is not significant as long as all of the other p... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
One other thing to keep in mind is that the tests on the individual coefficients each assume that all of the other predictors are in the model. In other words each predictor is not significant as long |
2,374 | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | One way to understand this is the geometry of least squares as @StasK suggests.
Another is to realize it means that X is related to Y when controlling for the other variables, but not alone. You say X relates to unique variance in Y. This is right. The unique variance in Y, though, is different from the total variance... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | One way to understand this is the geometry of least squares as @StasK suggests.
Another is to realize it means that X is related to Y when controlling for the other variables, but not alone. You say | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
One way to understand this is the geometry of least squares as @StasK suggests.
Another is to realize it means that X is related to Y when controlling for the other variables, but not alone. You say X relates to unique va... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests?
One way to understand this is the geometry of least squares as @StasK suggests.
Another is to realize it means that X is related to Y when controlling for the other variables, but not alone. You say |
2,375 | Why haven't robust (and resistant) statistics replaced classical techniques? | Researchers want small p-values, and you can get smaller p-values if you use methods that make stronger distributional assumptions. In other words, non-robust methods let you publish more papers. Of course more of these papers may be false positives, but a publication is a publication. That's a cynical explanation, bu... | Why haven't robust (and resistant) statistics replaced classical techniques? | Researchers want small p-values, and you can get smaller p-values if you use methods that make stronger distributional assumptions. In other words, non-robust methods let you publish more papers. Of c | Why haven't robust (and resistant) statistics replaced classical techniques?
Researchers want small p-values, and you can get smaller p-values if you use methods that make stronger distributional assumptions. In other words, non-robust methods let you publish more papers. Of course more of these papers may be false pos... | Why haven't robust (and resistant) statistics replaced classical techniques?
Researchers want small p-values, and you can get smaller p-values if you use methods that make stronger distributional assumptions. In other words, non-robust methods let you publish more papers. Of c |
2,376 | Why haven't robust (and resistant) statistics replaced classical techniques? | So 'classical models' (whatever they are - I assume you mean something like simple models taught in textbooks and estimated by ML) fail on some, perhaps many, real world data sets.
If a model fails then there are two basic approaches to fixing it:
Make fewer assumptions (less model)
Make more assumptions (more model... | Why haven't robust (and resistant) statistics replaced classical techniques? | So 'classical models' (whatever they are - I assume you mean something like simple models taught in textbooks and estimated by ML) fail on some, perhaps many, real world data sets.
If a model fails | Why haven't robust (and resistant) statistics replaced classical techniques?
So 'classical models' (whatever they are - I assume you mean something like simple models taught in textbooks and estimated by ML) fail on some, perhaps many, real world data sets.
If a model fails then there are two basic approaches to fixi... | Why haven't robust (and resistant) statistics replaced classical techniques?
So 'classical models' (whatever they are - I assume you mean something like simple models taught in textbooks and estimated by ML) fail on some, perhaps many, real world data sets.
If a model fails |
2,377 | Why haven't robust (and resistant) statistics replaced classical techniques? | I would suggest that it's a lag in teaching. Most people either learn statistics at college or University. If statistics is not your first degree and instead did a mathematics or computer science degree then you probably only cover the fundamental statistics modules:
Probability
Hypothesis testing
Regression
This me... | Why haven't robust (and resistant) statistics replaced classical techniques? | I would suggest that it's a lag in teaching. Most people either learn statistics at college or University. If statistics is not your first degree and instead did a mathematics or computer science degr | Why haven't robust (and resistant) statistics replaced classical techniques?
I would suggest that it's a lag in teaching. Most people either learn statistics at college or University. If statistics is not your first degree and instead did a mathematics or computer science degree then you probably only cover the fundame... | Why haven't robust (and resistant) statistics replaced classical techniques?
I would suggest that it's a lag in teaching. Most people either learn statistics at college or University. If statistics is not your first degree and instead did a mathematics or computer science degr |
2,378 | Why haven't robust (and resistant) statistics replaced classical techniques? | Statistics is a tool for non-statistical-minded researchers, and they just don't care.
I once tried to help with a Medicine article my ex-wife was co-authoring. I wrote several pages describing the data, what it suggested, why certain observations had been excluded from the study... and the lead researcher, a doctor, t... | Why haven't robust (and resistant) statistics replaced classical techniques? | Statistics is a tool for non-statistical-minded researchers, and they just don't care.
I once tried to help with a Medicine article my ex-wife was co-authoring. I wrote several pages describing the da | Why haven't robust (and resistant) statistics replaced classical techniques?
Statistics is a tool for non-statistical-minded researchers, and they just don't care.
I once tried to help with a Medicine article my ex-wife was co-authoring. I wrote several pages describing the data, what it suggested, why certain observat... | Why haven't robust (and resistant) statistics replaced classical techniques?
Statistics is a tool for non-statistical-minded researchers, and they just don't care.
I once tried to help with a Medicine article my ex-wife was co-authoring. I wrote several pages describing the da |
2,379 | Why haven't robust (and resistant) statistics replaced classical techniques? | Anyone trained in statistical data analysis at a reasonable level uses the concepts of robust statistics on a regular basis. Most researchers know enough to look for serious outliers and data recording errors; the policy of removing suspect data points goes back well into the 19th century with Lord Rayleigh, G.G. Stoke... | Why haven't robust (and resistant) statistics replaced classical techniques? | Anyone trained in statistical data analysis at a reasonable level uses the concepts of robust statistics on a regular basis. Most researchers know enough to look for serious outliers and data recordin | Why haven't robust (and resistant) statistics replaced classical techniques?
Anyone trained in statistical data analysis at a reasonable level uses the concepts of robust statistics on a regular basis. Most researchers know enough to look for serious outliers and data recording errors; the policy of removing suspect da... | Why haven't robust (and resistant) statistics replaced classical techniques?
Anyone trained in statistical data analysis at a reasonable level uses the concepts of robust statistics on a regular basis. Most researchers know enough to look for serious outliers and data recordin |
2,380 | Why haven't robust (and resistant) statistics replaced classical techniques? | I Give an answer in two directions:
things that are robust are not necessarily labeled robust. If you believe robustness against everything exists then you are naive.
Statistical approaches that leave the problem of robustness appart are sometime not adapted to the real world but are often more valuable (as a concep... | Why haven't robust (and resistant) statistics replaced classical techniques? | I Give an answer in two directions:
things that are robust are not necessarily labeled robust. If you believe robustness against everything exists then you are naive.
Statistical approaches that le | Why haven't robust (and resistant) statistics replaced classical techniques?
I Give an answer in two directions:
things that are robust are not necessarily labeled robust. If you believe robustness against everything exists then you are naive.
Statistical approaches that leave the problem of robustness appart are so... | Why haven't robust (and resistant) statistics replaced classical techniques?
I Give an answer in two directions:
things that are robust are not necessarily labeled robust. If you believe robustness against everything exists then you are naive.
Statistical approaches that le |
2,381 | Why haven't robust (and resistant) statistics replaced classical techniques? | As someone who has learned a little bit of statistics for my own research, I'll guess that the reasons are pedagogical and inertial.
I've observed within my own field that the order in which topics are taught reflects the history of the field. Those ideas which came first are taught first, and so on. For people who onl... | Why haven't robust (and resistant) statistics replaced classical techniques? | As someone who has learned a little bit of statistics for my own research, I'll guess that the reasons are pedagogical and inertial.
I've observed within my own field that the order in which topics ar | Why haven't robust (and resistant) statistics replaced classical techniques?
As someone who has learned a little bit of statistics for my own research, I'll guess that the reasons are pedagogical and inertial.
I've observed within my own field that the order in which topics are taught reflects the history of the field.... | Why haven't robust (and resistant) statistics replaced classical techniques?
As someone who has learned a little bit of statistics for my own research, I'll guess that the reasons are pedagogical and inertial.
I've observed within my own field that the order in which topics ar |
2,382 | Why haven't robust (and resistant) statistics replaced classical techniques? | Wooldridge "Introductory Econometrics - A Modern Approach" 2E p.261.
If Heteroskedasticity-robust standard errors are valid more often than the usual OLS standard errors, why do we bother we the usual standard errors at all?...One reason they are still used in cross sectional work is that, if the homoskedasticity assum... | Why haven't robust (and resistant) statistics replaced classical techniques? | Wooldridge "Introductory Econometrics - A Modern Approach" 2E p.261.
If Heteroskedasticity-robust standard errors are valid more often than the usual OLS standard errors, why do we bother we the usual | Why haven't robust (and resistant) statistics replaced classical techniques?
Wooldridge "Introductory Econometrics - A Modern Approach" 2E p.261.
If Heteroskedasticity-robust standard errors are valid more often than the usual OLS standard errors, why do we bother we the usual standard errors at all?...One reason they ... | Why haven't robust (and resistant) statistics replaced classical techniques?
Wooldridge "Introductory Econometrics - A Modern Approach" 2E p.261.
If Heteroskedasticity-robust standard errors are valid more often than the usual OLS standard errors, why do we bother we the usual |
2,383 | Why haven't robust (and resistant) statistics replaced classical techniques? | While they're not mutually exclusive, I think the growing popularity of Bayesian statistics is part of it. Bayesian statistics can achieve a lot of the same goals through priors and model averaging, and tend to be a bit more robust in practice. | Why haven't robust (and resistant) statistics replaced classical techniques? | While they're not mutually exclusive, I think the growing popularity of Bayesian statistics is part of it. Bayesian statistics can achieve a lot of the same goals through priors and model averaging, | Why haven't robust (and resistant) statistics replaced classical techniques?
While they're not mutually exclusive, I think the growing popularity of Bayesian statistics is part of it. Bayesian statistics can achieve a lot of the same goals through priors and model averaging, and tend to be a bit more robust in practic... | Why haven't robust (and resistant) statistics replaced classical techniques?
While they're not mutually exclusive, I think the growing popularity of Bayesian statistics is part of it. Bayesian statistics can achieve a lot of the same goals through priors and model averaging, |
2,384 | Why haven't robust (and resistant) statistics replaced classical techniques? | I'm not statistician, my experience in statistics is fairly limited, I just use robust statistics in computer vision/3d reconstruction/pose estimation. Here is my take on the problem from the user point of view:
First, robust statistics used a lot in engineering and science without calling it "robust statistics". A lot... | Why haven't robust (and resistant) statistics replaced classical techniques? | I'm not statistician, my experience in statistics is fairly limited, I just use robust statistics in computer vision/3d reconstruction/pose estimation. Here is my take on the problem from the user poi | Why haven't robust (and resistant) statistics replaced classical techniques?
I'm not statistician, my experience in statistics is fairly limited, I just use robust statistics in computer vision/3d reconstruction/pose estimation. Here is my take on the problem from the user point of view:
First, robust statistics used a... | Why haven't robust (and resistant) statistics replaced classical techniques?
I'm not statistician, my experience in statistics is fairly limited, I just use robust statistics in computer vision/3d reconstruction/pose estimation. Here is my take on the problem from the user poi |
2,385 | Why haven't robust (and resistant) statistics replaced classical techniques? | My knowledge of robust estimators is solely in regards to robust standard errors for regression parameters so my comment will only be in regards to those. I would suggest people read this article,
On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors"
by: Freedman, A. David
The American Statistician, ... | Why haven't robust (and resistant) statistics replaced classical techniques? | My knowledge of robust estimators is solely in regards to robust standard errors for regression parameters so my comment will only be in regards to those. I would suggest people read this article,
On | Why haven't robust (and resistant) statistics replaced classical techniques?
My knowledge of robust estimators is solely in regards to robust standard errors for regression parameters so my comment will only be in regards to those. I would suggest people read this article,
On The So-Called "Huber Sandwich Estimator" an... | Why haven't robust (and resistant) statistics replaced classical techniques?
My knowledge of robust estimators is solely in regards to robust standard errors for regression parameters so my comment will only be in regards to those. I would suggest people read this article,
On |
2,386 | Why haven't robust (and resistant) statistics replaced classical techniques? | The calculus and probability needed for robust statistics is (usually) harder, so (a) there is less theory and (b) it is harder to grasp. | Why haven't robust (and resistant) statistics replaced classical techniques? | The calculus and probability needed for robust statistics is (usually) harder, so (a) there is less theory and (b) it is harder to grasp. | Why haven't robust (and resistant) statistics replaced classical techniques?
The calculus and probability needed for robust statistics is (usually) harder, so (a) there is less theory and (b) it is harder to grasp. | Why haven't robust (and resistant) statistics replaced classical techniques?
The calculus and probability needed for robust statistics is (usually) harder, so (a) there is less theory and (b) it is harder to grasp. |
2,387 | Why haven't robust (and resistant) statistics replaced classical techniques? | I am surprised to see the Gauss-Markov theorem is not mentioned in this long list of answers, afaics:
In a linear model with spherical errors (which along the way includes an assumption of no outliers, via a finite error variance), OLS is efficient in a class of linear unbiased estimators - there are (restrictive, to b... | Why haven't robust (and resistant) statistics replaced classical techniques? | I am surprised to see the Gauss-Markov theorem is not mentioned in this long list of answers, afaics:
In a linear model with spherical errors (which along the way includes an assumption of no outliers | Why haven't robust (and resistant) statistics replaced classical techniques?
I am surprised to see the Gauss-Markov theorem is not mentioned in this long list of answers, afaics:
In a linear model with spherical errors (which along the way includes an assumption of no outliers, via a finite error variance), OLS is effi... | Why haven't robust (and resistant) statistics replaced classical techniques?
I am surprised to see the Gauss-Markov theorem is not mentioned in this long list of answers, afaics:
In a linear model with spherical errors (which along the way includes an assumption of no outliers |
2,388 | Why haven't robust (and resistant) statistics replaced classical techniques? | My guess would be that robust statistics are never sufficient i.e. to be robust these statistics skip some of the information about the distribution. And I suspect that it is not always a good thing.
In other words there's a trade-off between robustness and loss of information.
E.g. the median is robust because (unlike... | Why haven't robust (and resistant) statistics replaced classical techniques? | My guess would be that robust statistics are never sufficient i.e. to be robust these statistics skip some of the information about the distribution. And I suspect that it is not always a good thing.
| Why haven't robust (and resistant) statistics replaced classical techniques?
My guess would be that robust statistics are never sufficient i.e. to be robust these statistics skip some of the information about the distribution. And I suspect that it is not always a good thing.
In other words there's a trade-off between ... | Why haven't robust (and resistant) statistics replaced classical techniques?
My guess would be that robust statistics are never sufficient i.e. to be robust these statistics skip some of the information about the distribution. And I suspect that it is not always a good thing.
|
2,389 | What is the lasso in regression analysis? | The LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method that involves penalizing the absolute size of the regression coefficients.
By penalizing (or equivalently constraining the sum of the absolute values of the estimates) you end up in a situation where some of the parameter estimates may ... | What is the lasso in regression analysis? | The LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method that involves penalizing the absolute size of the regression coefficients.
By penalizing (or equivalently constraini | What is the lasso in regression analysis?
The LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method that involves penalizing the absolute size of the regression coefficients.
By penalizing (or equivalently constraining the sum of the absolute values of the estimates) you end up in a situation ... | What is the lasso in regression analysis?
The LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method that involves penalizing the absolute size of the regression coefficients.
By penalizing (or equivalently constraini |
2,390 | What is the lasso in regression analysis? | In "normal" regression (OLS) the goal is to minimize the residual sum of squares (RSS) in order to estimate the coefficients
$$
\underset{\beta \in \mathbb{R}^p}{\operatorname{argmin}} \sum_{i=1}^{n} (Y_{i} - \sum_{j=1}^{p}X_{ij}\beta_{j})^{2}
$$
In case of LASSO regression you estimate the coefficients with a slightly... | What is the lasso in regression analysis? | In "normal" regression (OLS) the goal is to minimize the residual sum of squares (RSS) in order to estimate the coefficients
$$
\underset{\beta \in \mathbb{R}^p}{\operatorname{argmin}} \sum_{i=1}^{n} | What is the lasso in regression analysis?
In "normal" regression (OLS) the goal is to minimize the residual sum of squares (RSS) in order to estimate the coefficients
$$
\underset{\beta \in \mathbb{R}^p}{\operatorname{argmin}} \sum_{i=1}^{n} (Y_{i} - \sum_{j=1}^{p}X_{ij}\beta_{j})^{2}
$$
In case of LASSO regression you... | What is the lasso in regression analysis?
In "normal" regression (OLS) the goal is to minimize the residual sum of squares (RSS) in order to estimate the coefficients
$$
\underset{\beta \in \mathbb{R}^p}{\operatorname{argmin}} \sum_{i=1}^{n} |
2,391 | What is the lasso in regression analysis? | LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regression. As penalty increases more coefficients are becomes zero and vice Versa. It uses L1 normalisation technique in whic... | What is the lasso in regression analysis? | LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regres | What is the lasso in regression analysis?
LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regression. As penalty increases more coefficients are becomes zero and vice Versa. ... | What is the lasso in regression analysis?
LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regres |
2,392 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | About k-means specifically, you can use the Gap statistics. Basically, the idea is to compute a goodness of clustering measure based on average dispersion compared to a reference distribution for an increasing number of clusters.
More information can be found in the original paper:
Tibshirani, R., Walther, G., and
H... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | About k-means specifically, you can use the Gap statistics. Basically, the idea is to compute a goodness of clustering measure based on average dispersion compared to a reference distribution for an i | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
About k-means specifically, you can use the Gap statistics. Basically, the idea is to compute a goodness of clustering measure based on average dispersion compared to a reference distribution for an increasing number of c... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
About k-means specifically, you can use the Gap statistics. Basically, the idea is to compute a goodness of clustering measure based on average dispersion compared to a reference distribution for an i |
2,393 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | When are results meaningful anyway? In particular k-means results?
Fact is that k-means optimizes a certain mathematical statistic. There is no "meaningful" associated with this.
In particular in high dimensional data, the first question should be: is the Euclidean distance still meaningful? If not, don't use k-means. ... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | When are results meaningful anyway? In particular k-means results?
Fact is that k-means optimizes a certain mathematical statistic. There is no "meaningful" associated with this.
In particular in high | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
When are results meaningful anyway? In particular k-means results?
Fact is that k-means optimizes a certain mathematical statistic. There is no "meaningful" associated with this.
In particular in high dimensional data, th... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
When are results meaningful anyway? In particular k-means results?
Fact is that k-means optimizes a certain mathematical statistic. There is no "meaningful" associated with this.
In particular in high |
2,394 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | One way to quickly visualize whether high dimensional data exhibits enough clustering is to use t-Distributed Stochastic Neighbor Embedding (t-SNE). It projects the data to some low dimensional space (e.g. 2D, 3D) and does a pretty good job at keeping cluster structure if any.
E.g. MNIST data set:
Olivetti faces data ... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | One way to quickly visualize whether high dimensional data exhibits enough clustering is to use t-Distributed Stochastic Neighbor Embedding (t-SNE). It projects the data to some low dimensional space | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
One way to quickly visualize whether high dimensional data exhibits enough clustering is to use t-Distributed Stochastic Neighbor Embedding (t-SNE). It projects the data to some low dimensional space (e.g. 2D, 3D) and doe... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
One way to quickly visualize whether high dimensional data exhibits enough clustering is to use t-Distributed Stochastic Neighbor Embedding (t-SNE). It projects the data to some low dimensional space |
2,395 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | Surely, the ability to visually discern the clusters in a plotable number of dimensions is a doubtful criterion for the usefulness of a clustering algorithm, especially if this dimension reduction is done independently of the clustering itself (i.e.: in a vain attempt to find out if clustering will work).
In fact, clus... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | Surely, the ability to visually discern the clusters in a plotable number of dimensions is a doubtful criterion for the usefulness of a clustering algorithm, especially if this dimension reduction is | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
Surely, the ability to visually discern the clusters in a plotable number of dimensions is a doubtful criterion for the usefulness of a clustering algorithm, especially if this dimension reduction is done independently of... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
Surely, the ability to visually discern the clusters in a plotable number of dimensions is a doubtful criterion for the usefulness of a clustering algorithm, especially if this dimension reduction is |
2,396 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | I have just started using clustering algorithms recently, so hopefully someone more knowledgeable can provide a more complete answer, but here are some thoughts:
'Meaningful', as I'm sure you're aware, is very subjective. So whether the clustering is good enough is completely dependent upon why you need to cluster in ... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | I have just started using clustering algorithms recently, so hopefully someone more knowledgeable can provide a more complete answer, but here are some thoughts:
'Meaningful', as I'm sure you're aware | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
I have just started using clustering algorithms recently, so hopefully someone more knowledgeable can provide a more complete answer, but here are some thoughts:
'Meaningful', as I'm sure you're aware, is very subjective.... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
I have just started using clustering algorithms recently, so hopefully someone more knowledgeable can provide a more complete answer, but here are some thoughts:
'Meaningful', as I'm sure you're aware |
2,397 | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | To tell whether a clustering is meaningful, you can run an algorithm to count the number of clusters, and see if it outputs something greater than 1.
Like chl said, one cluster-counting algorithm is the gap statistic algorithm. Roughly, this computes the total cluster variance given your actual data, and compares it ag... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | To tell whether a clustering is meaningful, you can run an algorithm to count the number of clusters, and see if it outputs something greater than 1.
Like chl said, one cluster-counting algorithm is t | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
To tell whether a clustering is meaningful, you can run an algorithm to count the number of clusters, and see if it outputs something greater than 1.
Like chl said, one cluster-counting algorithm is the gap statistic algo... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results?
To tell whether a clustering is meaningful, you can run an algorithm to count the number of clusters, and see if it outputs something greater than 1.
Like chl said, one cluster-counting algorithm is t |
2,398 | ImageNet: what is top-1 and top-5 error rate? | [...] where the top-5 error rate is the fraction of test images for which
the correct label is not among the five labels considered most
probable by the mode.
First, you make a prediction using the CNN and obtain the predicted class multinomial distribution ($\sum p_{class} = 1$).
Now, in the case of the top-1 score, ... | ImageNet: what is top-1 and top-5 error rate? | [...] where the top-5 error rate is the fraction of test images for which
the correct label is not among the five labels considered most
probable by the mode.
First, you make a prediction using the C | ImageNet: what is top-1 and top-5 error rate?
[...] where the top-5 error rate is the fraction of test images for which
the correct label is not among the five labels considered most
probable by the mode.
First, you make a prediction using the CNN and obtain the predicted class multinomial distribution ($\sum p_{class... | ImageNet: what is top-1 and top-5 error rate?
[...] where the top-5 error rate is the fraction of test images for which
the correct label is not among the five labels considered most
probable by the mode.
First, you make a prediction using the C |
2,399 | ImageNet: what is top-1 and top-5 error rate? | Your classifier gives you a probability for each class. Lets say we had only "cat", "dog", "house", "mouse" as classes (in this order). Then the classifier gives somehting like
0.1; 0.2; 0.0; 0.7
as a result. The Top-1 class is "mouse". The top-2 classes are {mouse, dog}. If the correct class was "dog", it would be co... | ImageNet: what is top-1 and top-5 error rate? | Your classifier gives you a probability for each class. Lets say we had only "cat", "dog", "house", "mouse" as classes (in this order). Then the classifier gives somehting like
0.1; 0.2; 0.0; 0.7
as | ImageNet: what is top-1 and top-5 error rate?
Your classifier gives you a probability for each class. Lets say we had only "cat", "dog", "house", "mouse" as classes (in this order). Then the classifier gives somehting like
0.1; 0.2; 0.0; 0.7
as a result. The Top-1 class is "mouse". The top-2 classes are {mouse, dog}. ... | ImageNet: what is top-1 and top-5 error rate?
Your classifier gives you a probability for each class. Lets say we had only "cat", "dog", "house", "mouse" as classes (in this order). Then the classifier gives somehting like
0.1; 0.2; 0.0; 0.7
as |
2,400 | What are disadvantages of using the lasso for variable selection for regression? | There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variables should be in the model REGARDLESS of any measure of significance. Sometimes they are necessary control variables. ... | What are disadvantages of using the lasso for variable selection for regression? | There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variab | What are disadvantages of using the lasso for variable selection for regression?
There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variables should be in the model REGARDLESS o... | What are disadvantages of using the lasso for variable selection for regression?
There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variab |
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