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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1,101 | Why does a time series have to be stationary? | Time Series is about analysing the way values of a series are dependent on previous values. As SRKX suggested one can difference or de-trend or de-mean a non-stationary series but not unnecessarily!) to create a stationary series. ARMA analysis requires stationarity. $X$ is strictly stationary if the distribution of ... | Why does a time series have to be stationary? | Time Series is about analysing the way values of a series are dependent on previous values. As SRKX suggested one can difference or de-trend or de-mean a non-stationary series but not unnecessarily! | Why does a time series have to be stationary?
Time Series is about analysing the way values of a series are dependent on previous values. As SRKX suggested one can difference or de-trend or de-mean a non-stationary series but not unnecessarily!) to create a stationary series. ARMA analysis requires stationarity. $X$ ... | Why does a time series have to be stationary?
Time Series is about analysing the way values of a series are dependent on previous values. As SRKX suggested one can difference or de-trend or de-mean a non-stationary series but not unnecessarily! |
1,102 | Why does a time series have to be stationary? | In my view stochastic process is the process which is govern by three statistical properties which must be time -invariant .They are mean variance and auto correlation function.Though the first two doesn't tell anything about the evolution of the process in time ,so the third property which is auto-correlation function... | Why does a time series have to be stationary? | In my view stochastic process is the process which is govern by three statistical properties which must be time -invariant .They are mean variance and auto correlation function.Though the first two do | Why does a time series have to be stationary?
In my view stochastic process is the process which is govern by three statistical properties which must be time -invariant .They are mean variance and auto correlation function.Though the first two doesn't tell anything about the evolution of the process in time ,so the thi... | Why does a time series have to be stationary?
In my view stochastic process is the process which is govern by three statistical properties which must be time -invariant .They are mean variance and auto correlation function.Though the first two do |
1,103 | Why does a time series have to be stationary? | To solve anything we need to model the equations mathematically using statics.
To solve such equations it needs to be independent and stationary(not moving)
In stationary data only we can able to get insights and do mathematical operations(mean, variance etc..) for multi-purpose
In non-stationary, it is hard to get da... | Why does a time series have to be stationary? | To solve anything we need to model the equations mathematically using statics.
To solve such equations it needs to be independent and stationary(not moving)
In stationary data only we can able to get | Why does a time series have to be stationary?
To solve anything we need to model the equations mathematically using statics.
To solve such equations it needs to be independent and stationary(not moving)
In stationary data only we can able to get insights and do mathematical operations(mean, variance etc..) for multi-p... | Why does a time series have to be stationary?
To solve anything we need to model the equations mathematically using statics.
To solve such equations it needs to be independent and stationary(not moving)
In stationary data only we can able to get |
1,104 | Clustering on the output of t-SNE | The problem with t-SNE is that it does not preserve distances nor density. It only to some extent preserves nearest-neighbors. The difference is subtle, but affects any density- or distance based algorithm.
While clustering after t-SNE will sometimes (often?) work, you will never know whether the "clusters" you find ar... | Clustering on the output of t-SNE | The problem with t-SNE is that it does not preserve distances nor density. It only to some extent preserves nearest-neighbors. The difference is subtle, but affects any density- or distance based algo | Clustering on the output of t-SNE
The problem with t-SNE is that it does not preserve distances nor density. It only to some extent preserves nearest-neighbors. The difference is subtle, but affects any density- or distance based algorithm.
While clustering after t-SNE will sometimes (often?) work, you will never know ... | Clustering on the output of t-SNE
The problem with t-SNE is that it does not preserve distances nor density. It only to some extent preserves nearest-neighbors. The difference is subtle, but affects any density- or distance based algo |
1,105 | Clustering on the output of t-SNE | I would like to provide a somewhat dissenting opinion to the well argued (+1) and highly upvoted answer by @ErichSchubert. Erich does not recommend clustering on the t-SNE output, and shows some toy examples where it can be misleading. His suggestion is to apply clustering to the original data instead.
use t-SNE for v... | Clustering on the output of t-SNE | I would like to provide a somewhat dissenting opinion to the well argued (+1) and highly upvoted answer by @ErichSchubert. Erich does not recommend clustering on the t-SNE output, and shows some toy e | Clustering on the output of t-SNE
I would like to provide a somewhat dissenting opinion to the well argued (+1) and highly upvoted answer by @ErichSchubert. Erich does not recommend clustering on the t-SNE output, and shows some toy examples where it can be misleading. His suggestion is to apply clustering to the origi... | Clustering on the output of t-SNE
I would like to provide a somewhat dissenting opinion to the well argued (+1) and highly upvoted answer by @ErichSchubert. Erich does not recommend clustering on the t-SNE output, and shows some toy e |
1,106 | Clustering on the output of t-SNE | I think with large perplexity t-SNE can reconstruct the global topology, as indicated in https://distill.pub/2016/misread-tsne/.
From the fish image, I sampled 4000 points for t-SNE. With a large perplexity (2000), the fish image was virtually reconstructed.
Here is the original image.
Here is the image reconstructed... | Clustering on the output of t-SNE | I think with large perplexity t-SNE can reconstruct the global topology, as indicated in https://distill.pub/2016/misread-tsne/.
From the fish image, I sampled 4000 points for t-SNE. With a large per | Clustering on the output of t-SNE
I think with large perplexity t-SNE can reconstruct the global topology, as indicated in https://distill.pub/2016/misread-tsne/.
From the fish image, I sampled 4000 points for t-SNE. With a large perplexity (2000), the fish image was virtually reconstructed.
Here is the original image... | Clustering on the output of t-SNE
I think with large perplexity t-SNE can reconstruct the global topology, as indicated in https://distill.pub/2016/misread-tsne/.
From the fish image, I sampled 4000 points for t-SNE. With a large per |
1,107 | Clustering on the output of t-SNE | Based on the mathematical evidence which we have, this method could technically preserve distances! why do you all ignore this feature! t-SNE is converting the high-dimensional Euclidean distances between samples into conditional probabilities which represent similarities. I have tried t-SNE with more than 11,000 sampl... | Clustering on the output of t-SNE | Based on the mathematical evidence which we have, this method could technically preserve distances! why do you all ignore this feature! t-SNE is converting the high-dimensional Euclidean distances bet | Clustering on the output of t-SNE
Based on the mathematical evidence which we have, this method could technically preserve distances! why do you all ignore this feature! t-SNE is converting the high-dimensional Euclidean distances between samples into conditional probabilities which represent similarities. I have tried... | Clustering on the output of t-SNE
Based on the mathematical evidence which we have, this method could technically preserve distances! why do you all ignore this feature! t-SNE is converting the high-dimensional Euclidean distances bet |
1,108 | Clustering on the output of t-SNE | You could try the DBSCAN clustering algorithm. Also, the perplexty of tsne should be about the same size as the smallest expected cluster. | Clustering on the output of t-SNE | You could try the DBSCAN clustering algorithm. Also, the perplexty of tsne should be about the same size as the smallest expected cluster. | Clustering on the output of t-SNE
You could try the DBSCAN clustering algorithm. Also, the perplexty of tsne should be about the same size as the smallest expected cluster. | Clustering on the output of t-SNE
You could try the DBSCAN clustering algorithm. Also, the perplexty of tsne should be about the same size as the smallest expected cluster. |
1,109 | Clustering on the output of t-SNE | Personally, I have experienced this once, but not with t-SNE or PCA. My original data is in 15-dimensional space. Using UMAP to reduce it to 2D and 3D embeddings, I got 2 perfectly and visually seperable clusters on both 2D and 3D plots. Too good to be true. But when I "looked" at the orginal data from the persistence ... | Clustering on the output of t-SNE | Personally, I have experienced this once, but not with t-SNE or PCA. My original data is in 15-dimensional space. Using UMAP to reduce it to 2D and 3D embeddings, I got 2 perfectly and visually sepera | Clustering on the output of t-SNE
Personally, I have experienced this once, but not with t-SNE or PCA. My original data is in 15-dimensional space. Using UMAP to reduce it to 2D and 3D embeddings, I got 2 perfectly and visually seperable clusters on both 2D and 3D plots. Too good to be true. But when I "looked" at the ... | Clustering on the output of t-SNE
Personally, I have experienced this once, but not with t-SNE or PCA. My original data is in 15-dimensional space. Using UMAP to reduce it to 2D and 3D embeddings, I got 2 perfectly and visually sepera |
1,110 | Clustering on the output of t-SNE | for anyone who is looking into similar questions, I have performed DBSCAN(metric using cosine similarity) on word embeddings of 50 dimensions as well as tsne 2d dimensions. For my corpus containing 1600 lines, I have exactly the same clustering groups (same number of cluster, same items in the groups, same number of no... | Clustering on the output of t-SNE | for anyone who is looking into similar questions, I have performed DBSCAN(metric using cosine similarity) on word embeddings of 50 dimensions as well as tsne 2d dimensions. For my corpus containing 16 | Clustering on the output of t-SNE
for anyone who is looking into similar questions, I have performed DBSCAN(metric using cosine similarity) on word embeddings of 50 dimensions as well as tsne 2d dimensions. For my corpus containing 1600 lines, I have exactly the same clustering groups (same number of cluster, same item... | Clustering on the output of t-SNE
for anyone who is looking into similar questions, I have performed DBSCAN(metric using cosine similarity) on word embeddings of 50 dimensions as well as tsne 2d dimensions. For my corpus containing 16 |
1,111 | Should one remove highly correlated variables before doing PCA? | This expounds upon the insightful hint provided in a comment by @ttnphns.
Adjoining nearly correlated variables increases the contribution of their common underlying factor to the PCA. We can see this geometrically. Consider these data in the XY plane, shown as a point cloud:
There is little correlation, approximate... | Should one remove highly correlated variables before doing PCA? | This expounds upon the insightful hint provided in a comment by @ttnphns.
Adjoining nearly correlated variables increases the contribution of their common underlying factor to the PCA. We can see thi | Should one remove highly correlated variables before doing PCA?
This expounds upon the insightful hint provided in a comment by @ttnphns.
Adjoining nearly correlated variables increases the contribution of their common underlying factor to the PCA. We can see this geometrically. Consider these data in the XY plane, s... | Should one remove highly correlated variables before doing PCA?
This expounds upon the insightful hint provided in a comment by @ttnphns.
Adjoining nearly correlated variables increases the contribution of their common underlying factor to the PCA. We can see thi |
1,112 | Should one remove highly correlated variables before doing PCA? | I will further illustrate the same process and idea as @whuber did, but with the loading plots, - because loadings are the essense of PCA results.
Here is three 3 analyses. In the first, we have two variables, $X_1$ and $X_2$ (in this example, they do not correlate). In the second, we added $X_3$ which is almost a copy... | Should one remove highly correlated variables before doing PCA? | I will further illustrate the same process and idea as @whuber did, but with the loading plots, - because loadings are the essense of PCA results.
Here is three 3 analyses. In the first, we have two v | Should one remove highly correlated variables before doing PCA?
I will further illustrate the same process and idea as @whuber did, but with the loading plots, - because loadings are the essense of PCA results.
Here is three 3 analyses. In the first, we have two variables, $X_1$ and $X_2$ (in this example, they do not ... | Should one remove highly correlated variables before doing PCA?
I will further illustrate the same process and idea as @whuber did, but with the loading plots, - because loadings are the essense of PCA results.
Here is three 3 analyses. In the first, we have two v |
1,113 | Should one remove highly correlated variables before doing PCA? | Without details from your paper, I would conjecture that this discarding of highly-correlated variables was done merely to save off on computational power or workload. I cannot see a reason for why PCA would 'break' for highly correlated variables. Projecting data back onto the bases found by PCA has the effect of whit... | Should one remove highly correlated variables before doing PCA? | Without details from your paper, I would conjecture that this discarding of highly-correlated variables was done merely to save off on computational power or workload. I cannot see a reason for why PC | Should one remove highly correlated variables before doing PCA?
Without details from your paper, I would conjecture that this discarding of highly-correlated variables was done merely to save off on computational power or workload. I cannot see a reason for why PCA would 'break' for highly correlated variables. Project... | Should one remove highly correlated variables before doing PCA?
Without details from your paper, I would conjecture that this discarding of highly-correlated variables was done merely to save off on computational power or workload. I cannot see a reason for why PC |
1,114 | Should one remove highly correlated variables before doing PCA? | From my understanding correlated variables are ok, because PCA outputs vectors that are orthogonal. | Should one remove highly correlated variables before doing PCA? | From my understanding correlated variables are ok, because PCA outputs vectors that are orthogonal. | Should one remove highly correlated variables before doing PCA?
From my understanding correlated variables are ok, because PCA outputs vectors that are orthogonal. | Should one remove highly correlated variables before doing PCA?
From my understanding correlated variables are ok, because PCA outputs vectors that are orthogonal. |
1,115 | Should one remove highly correlated variables before doing PCA? | Well, it depends on your algorithm. Highly correlated variables may mean an ill-conditioned matrix. If you use an algorithm that's sensitive to that it might make sense. But I dare saying that most of the modern algorithms used for cranking out eigenvalues and eigenvectors are robust to this. Try removing the highly co... | Should one remove highly correlated variables before doing PCA? | Well, it depends on your algorithm. Highly correlated variables may mean an ill-conditioned matrix. If you use an algorithm that's sensitive to that it might make sense. But I dare saying that most of | Should one remove highly correlated variables before doing PCA?
Well, it depends on your algorithm. Highly correlated variables may mean an ill-conditioned matrix. If you use an algorithm that's sensitive to that it might make sense. But I dare saying that most of the modern algorithms used for cranking out eigenvalues... | Should one remove highly correlated variables before doing PCA?
Well, it depends on your algorithm. Highly correlated variables may mean an ill-conditioned matrix. If you use an algorithm that's sensitive to that it might make sense. But I dare saying that most of |
1,116 | Should one remove highly correlated variables before doing PCA? | Depends on what principle component selection method you use doesn't it?
I tend to use any principle component with an eigenvalue > 1. So it wouldn't effect me.
And from the examples above even the scree plot method would usually pick the right one. IF YOU KEEP ALL BEFORE THE ELBOW. However if you simply picked the pri... | Should one remove highly correlated variables before doing PCA? | Depends on what principle component selection method you use doesn't it?
I tend to use any principle component with an eigenvalue > 1. So it wouldn't effect me.
And from the examples above even the sc | Should one remove highly correlated variables before doing PCA?
Depends on what principle component selection method you use doesn't it?
I tend to use any principle component with an eigenvalue > 1. So it wouldn't effect me.
And from the examples above even the scree plot method would usually pick the right one. IF YOU... | Should one remove highly correlated variables before doing PCA?
Depends on what principle component selection method you use doesn't it?
I tend to use any principle component with an eigenvalue > 1. So it wouldn't effect me.
And from the examples above even the sc |
1,117 | Where should I place dropout layers in a neural network? | In the original paper that proposed dropout layers, by Hinton (2012), dropout (with p=0.5) was used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers. This became the most commonly used configuration.
More recent research has shown some value in applying dropo... | Where should I place dropout layers in a neural network? | In the original paper that proposed dropout layers, by Hinton (2012), dropout (with p=0.5) was used on each of the fully connected (dense) layers before the output; it was not used on the convolutiona | Where should I place dropout layers in a neural network?
In the original paper that proposed dropout layers, by Hinton (2012), dropout (with p=0.5) was used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers. This became the most commonly used configuration.
Mo... | Where should I place dropout layers in a neural network?
In the original paper that proposed dropout layers, by Hinton (2012), dropout (with p=0.5) was used on each of the fully connected (dense) layers before the output; it was not used on the convolutiona |
1,118 | Where should I place dropout layers in a neural network? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
In front of every linear projections. Refer to Srivast... | Where should I place dropout layers in a neural network? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Where should I place dropout layers in a neural network?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | Where should I place dropout layers in a neural network?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
1,119 | Where should I place dropout layers in a neural network? | The original paper proposed dropout layers that were used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers.
We must not use dropout layer after convolutional layer as we slide the filter over the width and height of the input image we produce a 2-dimensional ... | Where should I place dropout layers in a neural network? | The original paper proposed dropout layers that were used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers.
We must not use dropout layer a | Where should I place dropout layers in a neural network?
The original paper proposed dropout layers that were used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers.
We must not use dropout layer after convolutional layer as we slide the filter over the width ... | Where should I place dropout layers in a neural network?
The original paper proposed dropout layers that were used on each of the fully connected (dense) layers before the output; it was not used on the convolutional layers.
We must not use dropout layer a |
1,120 | Where should I place dropout layers in a neural network? | Some people interpret the dropout enabled neural network as an approximation of Bayesian Neural Network. And we can see this problem from the Bayesian perspective or treat such networks as stochastic artificial neural networks.
Artificial neural network
An artificial neural network maps some inputs/features to the outp... | Where should I place dropout layers in a neural network? | Some people interpret the dropout enabled neural network as an approximation of Bayesian Neural Network. And we can see this problem from the Bayesian perspective or treat such networks as stochastic | Where should I place dropout layers in a neural network?
Some people interpret the dropout enabled neural network as an approximation of Bayesian Neural Network. And we can see this problem from the Bayesian perspective or treat such networks as stochastic artificial neural networks.
Artificial neural network
An artifi... | Where should I place dropout layers in a neural network?
Some people interpret the dropout enabled neural network as an approximation of Bayesian Neural Network. And we can see this problem from the Bayesian perspective or treat such networks as stochastic |
1,121 | Where should I place dropout layers in a neural network? | You apply dropout after the non-linear activation function.
Sources for this:
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Formula on
page 1933 and diagram on the next page.
https://sebastianraschka.com/faq/docs/dropout-activation.html
https://pgaleone.eu/deep-learning/regularization/2017/01/10/anaysis-o... | Where should I place dropout layers in a neural network? | You apply dropout after the non-linear activation function.
Sources for this:
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Formula on
page 1933 and diagram on the next page.
https://seb | Where should I place dropout layers in a neural network?
You apply dropout after the non-linear activation function.
Sources for this:
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Formula on
page 1933 and diagram on the next page.
https://sebastianraschka.com/faq/docs/dropout-activation.html
https://pgal... | Where should I place dropout layers in a neural network?
You apply dropout after the non-linear activation function.
Sources for this:
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - Formula on
page 1933 and diagram on the next page.
https://seb |
1,122 | Where should I place dropout layers in a neural network? | For transformers I think you should do it like this:
According to the original paper (https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) they say:
Residual Dropout We apply dropout [27] to the output of each sub-layer, before it is added to the
sub-layer input and normalized. In additio... | Where should I place dropout layers in a neural network? | For transformers I think you should do it like this:
According to the original paper (https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) they say:
Residual Dropout We | Where should I place dropout layers in a neural network?
For transformers I think you should do it like this:
According to the original paper (https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) they say:
Residual Dropout We apply dropout [27] to the output of each sub-layer, before it i... | Where should I place dropout layers in a neural network?
For transformers I think you should do it like this:
According to the original paper (https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) they say:
Residual Dropout We |
1,123 | What is the .632+ rule in bootstrapping? | I will get to the 0.632 estimator, but it'll be a somewhat long development:
Suppose we want to predict $Y$ with $X$ using the function $f$, where $f$ may depend on some parameters that are estimated using the data $(\mathbf{Y}, \mathbf{X})$, e.g. $f(\mathbf{X}) = \mathbf{X}\mathbf{\beta}$
A naïve estimate of predicti... | What is the .632+ rule in bootstrapping? | I will get to the 0.632 estimator, but it'll be a somewhat long development:
Suppose we want to predict $Y$ with $X$ using the function $f$, where $f$ may depend on some parameters that are estimated | What is the .632+ rule in bootstrapping?
I will get to the 0.632 estimator, but it'll be a somewhat long development:
Suppose we want to predict $Y$ with $X$ using the function $f$, where $f$ may depend on some parameters that are estimated using the data $(\mathbf{Y}, \mathbf{X})$, e.g. $f(\mathbf{X}) = \mathbf{X}\mat... | What is the .632+ rule in bootstrapping?
I will get to the 0.632 estimator, but it'll be a somewhat long development:
Suppose we want to predict $Y$ with $X$ using the function $f$, where $f$ may depend on some parameters that are estimated |
1,124 | What is the .632+ rule in bootstrapping? | You will find more information in section 3 of this1 paper. But to summarize, if you call $S$ a sample of $n$ numbers from $\{1:n\}$ drawn randomly and with replacement, $S$ contains on average approximately $(1-e^{-1})\,n \approx 0.63212056\, n$ unique elements.
The reasoning is as follows. We populate $S=\{s_1,\ldots... | What is the .632+ rule in bootstrapping? | You will find more information in section 3 of this1 paper. But to summarize, if you call $S$ a sample of $n$ numbers from $\{1:n\}$ drawn randomly and with replacement, $S$ contains on average approx | What is the .632+ rule in bootstrapping?
You will find more information in section 3 of this1 paper. But to summarize, if you call $S$ a sample of $n$ numbers from $\{1:n\}$ drawn randomly and with replacement, $S$ contains on average approximately $(1-e^{-1})\,n \approx 0.63212056\, n$ unique elements.
The reasoning i... | What is the .632+ rule in bootstrapping?
You will find more information in section 3 of this1 paper. But to summarize, if you call $S$ a sample of $n$ numbers from $\{1:n\}$ drawn randomly and with replacement, $S$ contains on average approx |
1,125 | What is the .632+ rule in bootstrapping? | In my experience, primarily based on simulations, the 0.632 and 0.632+ bootstrap variants were needed only because of severe problems caused by the use of an improper accuracy scoring rule, namely the proportion "classified" correctly. When you use proper (e.g., deviance-based or Brier score) or semi-proper (e.g., $c$... | What is the .632+ rule in bootstrapping? | In my experience, primarily based on simulations, the 0.632 and 0.632+ bootstrap variants were needed only because of severe problems caused by the use of an improper accuracy scoring rule, namely the | What is the .632+ rule in bootstrapping?
In my experience, primarily based on simulations, the 0.632 and 0.632+ bootstrap variants were needed only because of severe problems caused by the use of an improper accuracy scoring rule, namely the proportion "classified" correctly. When you use proper (e.g., deviance-based ... | What is the .632+ rule in bootstrapping?
In my experience, primarily based on simulations, the 0.632 and 0.632+ bootstrap variants were needed only because of severe problems caused by the use of an improper accuracy scoring rule, namely the |
1,126 | What is the .632+ rule in bootstrapping? | Those answers are very useful. I couldn't find a way to demonstrate it with maths so I wrote some Python code which works quite well though:
from numpy import mean
from numpy.random import choice
N = 3000
variables = range(N)
num_loop = 1000
# Proportion of remaining variables
p_var = []
... | What is the .632+ rule in bootstrapping? | Those answers are very useful. I couldn't find a way to demonstrate it with maths so I wrote some Python code which works quite well though:
from numpy import mean
from numpy.random import cho | What is the .632+ rule in bootstrapping?
Those answers are very useful. I couldn't find a way to demonstrate it with maths so I wrote some Python code which works quite well though:
from numpy import mean
from numpy.random import choice
N = 3000
variables = range(N)
num_loop = 1000
# Proporti... | What is the .632+ rule in bootstrapping?
Those answers are very useful. I couldn't find a way to demonstrate it with maths so I wrote some Python code which works quite well though:
from numpy import mean
from numpy.random import cho |
1,127 | What is the .632+ rule in bootstrapping? | I was struggling with this concept of 632+. The given answers here clear up some things for me but I find it all rather technical. For those of you that are at my level I'll try to explain it:
The 632+ bootstrap trains en test models with a bootstraps of your dataset and then calculates scores, for example accuracy, fo... | What is the .632+ rule in bootstrapping? | I was struggling with this concept of 632+. The given answers here clear up some things for me but I find it all rather technical. For those of you that are at my level I'll try to explain it:
The 632 | What is the .632+ rule in bootstrapping?
I was struggling with this concept of 632+. The given answers here clear up some things for me but I find it all rather technical. For those of you that are at my level I'll try to explain it:
The 632+ bootstrap trains en test models with a bootstraps of your dataset and then ca... | What is the .632+ rule in bootstrapping?
I was struggling with this concept of 632+. The given answers here clear up some things for me but I find it all rather technical. For those of you that are at my level I'll try to explain it:
The 632 |
1,128 | KL divergence between two univariate Gaussians | OK, my bad. The error is in the last equation:
\begin{align}
KL(p, q) &= - \int p(x) \log q(x) dx + \int p(x) \log p(x) dx\\\\
&=\frac{1}{2} \log (2 \pi \sigma_2^2) + \frac{\sigma_1^2 + (\mu_1 - \mu_2)^2}{2 \sigma_2^2} - \frac{1}{2} (1 + \log 2 \pi \sigma_1^2)\\\\
&= \log \frac{\sigma_2}{\sigma_1} + \frac{\sigma_1^2 + ... | KL divergence between two univariate Gaussians | OK, my bad. The error is in the last equation:
\begin{align}
KL(p, q) &= - \int p(x) \log q(x) dx + \int p(x) \log p(x) dx\\\\
&=\frac{1}{2} \log (2 \pi \sigma_2^2) + \frac{\sigma_1^2 + (\mu_1 - \mu_2 | KL divergence between two univariate Gaussians
OK, my bad. The error is in the last equation:
\begin{align}
KL(p, q) &= - \int p(x) \log q(x) dx + \int p(x) \log p(x) dx\\\\
&=\frac{1}{2} \log (2 \pi \sigma_2^2) + \frac{\sigma_1^2 + (\mu_1 - \mu_2)^2}{2 \sigma_2^2} - \frac{1}{2} (1 + \log 2 \pi \sigma_1^2)\\\\
&= \log ... | KL divergence between two univariate Gaussians
OK, my bad. The error is in the last equation:
\begin{align}
KL(p, q) &= - \int p(x) \log q(x) dx + \int p(x) \log p(x) dx\\\\
&=\frac{1}{2} \log (2 \pi \sigma_2^2) + \frac{\sigma_1^2 + (\mu_1 - \mu_2 |
1,129 | KL divergence between two univariate Gaussians | I did not have a look at your calculation but here is mine with a lot of details.
Suppose $p$ is the density of a normal random variable with mean $\mu_1$ and variance $\sigma^2_1$, and that $q$ is the density of a normal random variable with mean $\mu_2$ and variance $\sigma^2_2$.
The Kullback-Leibler distance from $q... | KL divergence between two univariate Gaussians | I did not have a look at your calculation but here is mine with a lot of details.
Suppose $p$ is the density of a normal random variable with mean $\mu_1$ and variance $\sigma^2_1$, and that $q$ is th | KL divergence between two univariate Gaussians
I did not have a look at your calculation but here is mine with a lot of details.
Suppose $p$ is the density of a normal random variable with mean $\mu_1$ and variance $\sigma^2_1$, and that $q$ is the density of a normal random variable with mean $\mu_2$ and variance $\si... | KL divergence between two univariate Gaussians
I did not have a look at your calculation but here is mine with a lot of details.
Suppose $p$ is the density of a normal random variable with mean $\mu_1$ and variance $\sigma^2_1$, and that $q$ is th |
1,130 | Is Facebook coming to an end? | The answers so far have focused on the data itself, which makes sense with the site this is on, and the flaws about it.
But I'm a computational/mathematical epidemiologist by inclination, so I'm also going to talk about the model itself for a little bit, because it's also relevant to the discussion.
In my mind, the big... | Is Facebook coming to an end? | The answers so far have focused on the data itself, which makes sense with the site this is on, and the flaws about it.
But I'm a computational/mathematical epidemiologist by inclination, so I'm also | Is Facebook coming to an end?
The answers so far have focused on the data itself, which makes sense with the site this is on, and the flaws about it.
But I'm a computational/mathematical epidemiologist by inclination, so I'm also going to talk about the model itself for a little bit, because it's also relevant to the d... | Is Facebook coming to an end?
The answers so far have focused on the data itself, which makes sense with the site this is on, and the flaws about it.
But I'm a computational/mathematical epidemiologist by inclination, so I'm also |
1,131 | Is Facebook coming to an end? | My primary concern with this paper is that it focuses primarily on Google search results. It is a well-established fact that smartphone use is on the rise (Pew Internet, Brandwatch), and traditional computer sales are declining (possibly just due to old computers still functioning) (Slate, ExtremeTech), as more people ... | Is Facebook coming to an end? | My primary concern with this paper is that it focuses primarily on Google search results. It is a well-established fact that smartphone use is on the rise (Pew Internet, Brandwatch), and traditional c | Is Facebook coming to an end?
My primary concern with this paper is that it focuses primarily on Google search results. It is a well-established fact that smartphone use is on the rise (Pew Internet, Brandwatch), and traditional computer sales are declining (possibly just due to old computers still functioning) (Slate,... | Is Facebook coming to an end?
My primary concern with this paper is that it focuses primarily on Google search results. It is a well-established fact that smartphone use is on the rise (Pew Internet, Brandwatch), and traditional c |
1,132 | Is Facebook coming to an end? | Well, this paper establishes the fact that the number of Google searches on Facebook fits a certain curve nicely. So at best it can predict that the searches on Facebook will decline by 80%. Which might be feasible, because Facebook might become so ubiquitous that nobody would need to search about it.
The problem with... | Is Facebook coming to an end? | Well, this paper establishes the fact that the number of Google searches on Facebook fits a certain curve nicely. So at best it can predict that the searches on Facebook will decline by 80%. Which mig | Is Facebook coming to an end?
Well, this paper establishes the fact that the number of Google searches on Facebook fits a certain curve nicely. So at best it can predict that the searches on Facebook will decline by 80%. Which might be feasible, because Facebook might become so ubiquitous that nobody would need to sear... | Is Facebook coming to an end?
Well, this paper establishes the fact that the number of Google searches on Facebook fits a certain curve nicely. So at best it can predict that the searches on Facebook will decline by 80%. Which mig |
1,133 | Is Facebook coming to an end? | Google Trend in my opinion can't produce a good data set for this case of study. Google trend shows how often a term is searched with Google so there are at least two reasons for raising some doubts about the prevision:
We don't know if the user searches on Google Facebook to log in or if he searches information about... | Is Facebook coming to an end? | Google Trend in my opinion can't produce a good data set for this case of study. Google trend shows how often a term is searched with Google so there are at least two reasons for raising some doubts a | Is Facebook coming to an end?
Google Trend in my opinion can't produce a good data set for this case of study. Google trend shows how often a term is searched with Google so there are at least two reasons for raising some doubts about the prevision:
We don't know if the user searches on Google Facebook to log in or if... | Is Facebook coming to an end?
Google Trend in my opinion can't produce a good data set for this case of study. Google trend shows how often a term is searched with Google so there are at least two reasons for raising some doubts a |
1,134 | Is Facebook coming to an end? | A few basic issues stand out with this paper:
It assumes correlation of search engine queries about a rising social network with the membership increases. This may have correlated in the past, but may not in the future.
There are very few new large social networks. You can almost count them on one hand. Friendster, My... | Is Facebook coming to an end? | A few basic issues stand out with this paper:
It assumes correlation of search engine queries about a rising social network with the membership increases. This may have correlated in the past, but ma | Is Facebook coming to an end?
A few basic issues stand out with this paper:
It assumes correlation of search engine queries about a rising social network with the membership increases. This may have correlated in the past, but may not in the future.
There are very few new large social networks. You can almost count th... | Is Facebook coming to an end?
A few basic issues stand out with this paper:
It assumes correlation of search engine queries about a rising social network with the membership increases. This may have correlated in the past, but ma |
1,135 | Is Facebook coming to an end? | The question isn't "if" but "when".
That it will end is already guaranteed.
http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html
I take umbrage with the use of the SIR model. It comes with assumptions.
One of the assumptions is that eventually everyone is "recovered". Infections ... | Is Facebook coming to an end? | The question isn't "if" but "when".
That it will end is already guaranteed.
http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html
I take umbrage with the use of th | Is Facebook coming to an end?
The question isn't "if" but "when".
That it will end is already guaranteed.
http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html
I take umbrage with the use of the SIR model. It comes with assumptions.
One of the assumptions is that eventually everyon... | Is Facebook coming to an end?
The question isn't "if" but "when".
That it will end is already guaranteed.
http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html
I take umbrage with the use of th |
1,136 | Is Facebook coming to an end? | To answer your question
This model and logic may have worked for MySpace, but is it valid for
any social network?
Probably not. Historical data can only predict future events if the 'environment' is similar. This paper assumes that the total of Google users and queries is a constant, which of course it is not. No... | Is Facebook coming to an end? | To answer your question
This model and logic may have worked for MySpace, but is it valid for
any social network?
Probably not. Historical data can only predict future events if the 'environment' | Is Facebook coming to an end?
To answer your question
This model and logic may have worked for MySpace, but is it valid for
any social network?
Probably not. Historical data can only predict future events if the 'environment' is similar. This paper assumes that the total of Google users and queries is a constant, ... | Is Facebook coming to an end?
To answer your question
This model and logic may have worked for MySpace, but is it valid for
any social network?
Probably not. Historical data can only predict future events if the 'environment' |
1,137 | Is Facebook coming to an end? | If we take a look at the map of social networks, there are some cases
that epidemic model applies.
http://vincos.it/world-map-of-social-networks/
The article could have some other examples (Friendster and Orkut are a good example of massive declination of its users) and also taking into account the fact that normally... | Is Facebook coming to an end? | If we take a look at the map of social networks, there are some cases
that epidemic model applies.
http://vincos.it/world-map-of-social-networks/
The article could have some other examples (Friendst | Is Facebook coming to an end?
If we take a look at the map of social networks, there are some cases
that epidemic model applies.
http://vincos.it/world-map-of-social-networks/
The article could have some other examples (Friendster and Orkut are a good example of massive declination of its users) and also taking into ... | Is Facebook coming to an end?
If we take a look at the map of social networks, there are some cases
that epidemic model applies.
http://vincos.it/world-map-of-social-networks/
The article could have some other examples (Friendst |
1,138 | Is Facebook coming to an end? | The answers here are excellent in picking apart the paper's weaknesses; I especially enjoyed @Fomite's critique of their use of SIR models. But it's now been 8 years since this question was asked, and 2017 has come and gone. So I thought it would be fun to revisit this and ask: what do the data show?
Well, facebook use... | Is Facebook coming to an end? | The answers here are excellent in picking apart the paper's weaknesses; I especially enjoyed @Fomite's critique of their use of SIR models. But it's now been 8 years since this question was asked, and | Is Facebook coming to an end?
The answers here are excellent in picking apart the paper's weaknesses; I especially enjoyed @Fomite's critique of their use of SIR models. But it's now been 8 years since this question was asked, and 2017 has come and gone. So I thought it would be fun to revisit this and ask: what do the... | Is Facebook coming to an end?
The answers here are excellent in picking apart the paper's weaknesses; I especially enjoyed @Fomite's critique of their use of SIR models. But it's now been 8 years since this question was asked, and |
1,139 | Why are neural networks becoming deeper, but not wider? | As a disclaimer, I work on neural nets in my research, but I generally use relatively small, shallow neural nets rather than the really deep networks at the cutting edge of research you cite in your question. I am not an expert on the quirks and peculiarities of very deep networks and I will defer to someone who is.
F... | Why are neural networks becoming deeper, but not wider? | As a disclaimer, I work on neural nets in my research, but I generally use relatively small, shallow neural nets rather than the really deep networks at the cutting edge of research you cite in your q | Why are neural networks becoming deeper, but not wider?
As a disclaimer, I work on neural nets in my research, but I generally use relatively small, shallow neural nets rather than the really deep networks at the cutting edge of research you cite in your question. I am not an expert on the quirks and peculiarities of ... | Why are neural networks becoming deeper, but not wider?
As a disclaimer, I work on neural nets in my research, but I generally use relatively small, shallow neural nets rather than the really deep networks at the cutting edge of research you cite in your q |
1,140 | Why are neural networks becoming deeper, but not wider? | I don't think there is a definite answer to your questions. But I think the conventional wisdom goes as following:
Basically, as the hypothesis space of a learning algorithm grows, the algorithm can learn richer and richer structures. But at the same time, the algorithm becomes more prone to overfitting and its genera... | Why are neural networks becoming deeper, but not wider? | I don't think there is a definite answer to your questions. But I think the conventional wisdom goes as following:
Basically, as the hypothesis space of a learning algorithm grows, the algorithm can | Why are neural networks becoming deeper, but not wider?
I don't think there is a definite answer to your questions. But I think the conventional wisdom goes as following:
Basically, as the hypothesis space of a learning algorithm grows, the algorithm can learn richer and richer structures. But at the same time, the al... | Why are neural networks becoming deeper, but not wider?
I don't think there is a definite answer to your questions. But I think the conventional wisdom goes as following:
Basically, as the hypothesis space of a learning algorithm grows, the algorithm can |
1,141 | Why are neural networks becoming deeper, but not wider? | Adding more features helps but the benefit quickly become marginal after a lot of features were added. That's one reason why tools like PCA work: a few components capture most variance in the features. Hence, adding more features after some point is almost useless.
On the other hand finding the right functional for ma... | Why are neural networks becoming deeper, but not wider? | Adding more features helps but the benefit quickly become marginal after a lot of features were added. That's one reason why tools like PCA work: a few components capture most variance in the features | Why are neural networks becoming deeper, but not wider?
Adding more features helps but the benefit quickly become marginal after a lot of features were added. That's one reason why tools like PCA work: a few components capture most variance in the features. Hence, adding more features after some point is almost useless... | Why are neural networks becoming deeper, but not wider?
Adding more features helps but the benefit quickly become marginal after a lot of features were added. That's one reason why tools like PCA work: a few components capture most variance in the features |
1,142 | Why are neural networks becoming deeper, but not wider? | For a densely connected neural net of depth $d$ and width $w$, the number of parameters (hence, RAM required to run or train the network) is $O(dw^2)$. Thus, if you only have a limited number of parameters, it often makes sense to prefer a large increase in depth over a small increase in width.
Why might you be trying ... | Why are neural networks becoming deeper, but not wider? | For a densely connected neural net of depth $d$ and width $w$, the number of parameters (hence, RAM required to run or train the network) is $O(dw^2)$. Thus, if you only have a limited number of param | Why are neural networks becoming deeper, but not wider?
For a densely connected neural net of depth $d$ and width $w$, the number of parameters (hence, RAM required to run or train the network) is $O(dw^2)$. Thus, if you only have a limited number of parameters, it often makes sense to prefer a large increase in depth ... | Why are neural networks becoming deeper, but not wider?
For a densely connected neural net of depth $d$ and width $w$, the number of parameters (hence, RAM required to run or train the network) is $O(dw^2)$. Thus, if you only have a limited number of param |
1,143 | Why are neural networks becoming deeper, but not wider? | I think you are get the in detail answer of the question through this paper name Impact of fully connected layers on performance of convolutional neural networks for image classification link - https://www.sciencedirect.com/science/article/pii/S0925231219313803.
It comes to the following conclusion -
In order to obtain... | Why are neural networks becoming deeper, but not wider? | I think you are get the in detail answer of the question through this paper name Impact of fully connected layers on performance of convolutional neural networks for image classification link - https: | Why are neural networks becoming deeper, but not wider?
I think you are get the in detail answer of the question through this paper name Impact of fully connected layers on performance of convolutional neural networks for image classification link - https://www.sciencedirect.com/science/article/pii/S0925231219313803.
I... | Why are neural networks becoming deeper, but not wider?
I think you are get the in detail answer of the question through this paper name Impact of fully connected layers on performance of convolutional neural networks for image classification link - https: |
1,144 | Why are neural networks becoming deeper, but not wider? | Currently, on GPUs - we use 32-bit float and with 512 features - combining them we already get quite imprecise. Going even wider is hence limited by numerics and precision of 32-bit float. Another thing is that we actually should probably go wider if we care about accuracy:
https://www.sciencedirect.com/science/article... | Why are neural networks becoming deeper, but not wider? | Currently, on GPUs - we use 32-bit float and with 512 features - combining them we already get quite imprecise. Going even wider is hence limited by numerics and precision of 32-bit float. Another thi | Why are neural networks becoming deeper, but not wider?
Currently, on GPUs - we use 32-bit float and with 512 features - combining them we already get quite imprecise. Going even wider is hence limited by numerics and precision of 32-bit float. Another thing is that we actually should probably go wider if we care about... | Why are neural networks becoming deeper, but not wider?
Currently, on GPUs - we use 32-bit float and with 512 features - combining them we already get quite imprecise. Going even wider is hence limited by numerics and precision of 32-bit float. Another thi |
1,145 | Removal of statistically significant intercept term increases $R^2$ in linear model | First of all, we should understand what the R software is doing when no intercept
is included in the model. Recall that the usual computation of $R^2$
when an intercept is present is
$$
R^2 = \frac{\sum_i (\hat y_i - \bar y)^2}{\sum_i (y_i - \bar
y)^2} = 1 - \frac{\sum_i (y_i - \hat y_i)^2}{\sum_i (y_i - \bar
y)^2} \>.... | Removal of statistically significant intercept term increases $R^2$ in linear model | First of all, we should understand what the R software is doing when no intercept
is included in the model. Recall that the usual computation of $R^2$
when an intercept is present is
$$
R^2 = \frac{\s | Removal of statistically significant intercept term increases $R^2$ in linear model
First of all, we should understand what the R software is doing when no intercept
is included in the model. Recall that the usual computation of $R^2$
when an intercept is present is
$$
R^2 = \frac{\sum_i (\hat y_i - \bar y)^2}{\sum_i (... | Removal of statistically significant intercept term increases $R^2$ in linear model
First of all, we should understand what the R software is doing when no intercept
is included in the model. Recall that the usual computation of $R^2$
when an intercept is present is
$$
R^2 = \frac{\s |
1,146 | Removal of statistically significant intercept term increases $R^2$ in linear model | I would base my decision on an information criteria such as the Akaike or Bayes-Schwarz criteria rather than R^2; even then I would not view these as absolute.
If you have a process where the slope is near zero and all of the data is far from the origin, your correct R^2 should be low as most of the variation in the ... | Removal of statistically significant intercept term increases $R^2$ in linear model | I would base my decision on an information criteria such as the Akaike or Bayes-Schwarz criteria rather than R^2; even then I would not view these as absolute.
If you have a process where the slope | Removal of statistically significant intercept term increases $R^2$ in linear model
I would base my decision on an information criteria such as the Akaike or Bayes-Schwarz criteria rather than R^2; even then I would not view these as absolute.
If you have a process where the slope is near zero and all of the data is ... | Removal of statistically significant intercept term increases $R^2$ in linear model
I would base my decision on an information criteria such as the Akaike or Bayes-Schwarz criteria rather than R^2; even then I would not view these as absolute.
If you have a process where the slope |
1,147 | Removal of statistically significant intercept term increases $R^2$ in linear model | The way in which the R software computes the R squared for the case of no intercept (see the answer by cardinal) produces inconsistent results.
Suppose that you have a single categorical explanatory variable that has only two categories (cat and dog).
Then you can write two completely equivalent regression models:
a r... | Removal of statistically significant intercept term increases $R^2$ in linear model | The way in which the R software computes the R squared for the case of no intercept (see the answer by cardinal) produces inconsistent results.
Suppose that you have a single categorical explanatory v | Removal of statistically significant intercept term increases $R^2$ in linear model
The way in which the R software computes the R squared for the case of no intercept (see the answer by cardinal) produces inconsistent results.
Suppose that you have a single categorical explanatory variable that has only two categories... | Removal of statistically significant intercept term increases $R^2$ in linear model
The way in which the R software computes the R squared for the case of no intercept (see the answer by cardinal) produces inconsistent results.
Suppose that you have a single categorical explanatory v |
1,148 | Difference between neural net weight decay and learning rate | The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes the weights to exponentially decay to zero, if no other update is scheduled.
So let's say that we have a cost or error fu... | Difference between neural net weight decay and learning rate | The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes t | Difference between neural net weight decay and learning rate
The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes the weights to exponentially decay to zero, if no other upda... | Difference between neural net weight decay and learning rate
The learning rate is a parameter that determines how much an updating step influences the current value of the weights. While weight decay is an additional term in the weight update rule that causes t |
1,149 | Difference between neural net weight decay and learning rate | In addition to @mrig's answer (+1), for many practical application of neural networks it is better to use a more advanced optimisation algorithm, such as Levenberg-Marquardt (small-medium sized networks) or scaled conjugate gradient descent (medium-large networks), as these will be much faster, and there is no need to ... | Difference between neural net weight decay and learning rate | In addition to @mrig's answer (+1), for many practical application of neural networks it is better to use a more advanced optimisation algorithm, such as Levenberg-Marquardt (small-medium sized networ | Difference between neural net weight decay and learning rate
In addition to @mrig's answer (+1), for many practical application of neural networks it is better to use a more advanced optimisation algorithm, such as Levenberg-Marquardt (small-medium sized networks) or scaled conjugate gradient descent (medium-large netw... | Difference between neural net weight decay and learning rate
In addition to @mrig's answer (+1), for many practical application of neural networks it is better to use a more advanced optimisation algorithm, such as Levenberg-Marquardt (small-medium sized networ |
1,150 | Difference between neural net weight decay and learning rate | So the answer given by @mrig is actually intuitively alright. But theoretically speaking what he has explained is L2 regularization. This was known as weight decay back in the day but now I think the literature is pretty clear about the fact. These two concepts have a subtle difference and learning this difference can ... | Difference between neural net weight decay and learning rate | So the answer given by @mrig is actually intuitively alright. But theoretically speaking what he has explained is L2 regularization. This was known as weight decay back in the day but now I think the | Difference between neural net weight decay and learning rate
So the answer given by @mrig is actually intuitively alright. But theoretically speaking what he has explained is L2 regularization. This was known as weight decay back in the day but now I think the literature is pretty clear about the fact. These two concep... | Difference between neural net weight decay and learning rate
So the answer given by @mrig is actually intuitively alright. But theoretically speaking what he has explained is L2 regularization. This was known as weight decay back in the day but now I think the |
1,151 | Difference between neural net weight decay and learning rate | In simple terms:
learning_rate: It controls how quickly or slowly a neural network model learns a problem.
See: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/
weight_decay: Is a regularisation technique used to avoid over-fitting.
See: https://metacademy.org/graphs/concepts/weight_... | Difference between neural net weight decay and learning rate | In simple terms:
learning_rate: It controls how quickly or slowly a neural network model learns a problem.
See: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/
wei | Difference between neural net weight decay and learning rate
In simple terms:
learning_rate: It controls how quickly or slowly a neural network model learns a problem.
See: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/
weight_decay: Is a regularisation technique used to avoid over... | Difference between neural net weight decay and learning rate
In simple terms:
learning_rate: It controls how quickly or slowly a neural network model learns a problem.
See: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/
wei |
1,152 | Obtaining knowledge from a random forest | Random Forests are hardly a black box. They are based on decision trees, which are very easy to interpret:
#Setup a binary classification problem
require(randomForest)
data(iris)
set.seed(1)
dat <- iris
dat$Species <- factor(ifelse(dat$Species=='virginica','virginica','other'))
trainrows <- runif(nrow(dat)) > 0.3
trai... | Obtaining knowledge from a random forest | Random Forests are hardly a black box. They are based on decision trees, which are very easy to interpret:
#Setup a binary classification problem
require(randomForest)
data(iris)
set.seed(1)
dat <- i | Obtaining knowledge from a random forest
Random Forests are hardly a black box. They are based on decision trees, which are very easy to interpret:
#Setup a binary classification problem
require(randomForest)
data(iris)
set.seed(1)
dat <- iris
dat$Species <- factor(ifelse(dat$Species=='virginica','virginica','other'))... | Obtaining knowledge from a random forest
Random Forests are hardly a black box. They are based on decision trees, which are very easy to interpret:
#Setup a binary classification problem
require(randomForest)
data(iris)
set.seed(1)
dat <- i |
1,153 | Obtaining knowledge from a random forest | Some time ago I had to justify a RF model-fit to some chemists in my company. I spent quite time trying different visualization techniques. During the process, I accidentally also came up with some new techniques which I put into an R package (forestFloor) specifically for random forest visualizations.
The classical ap... | Obtaining knowledge from a random forest | Some time ago I had to justify a RF model-fit to some chemists in my company. I spent quite time trying different visualization techniques. During the process, I accidentally also came up with some ne | Obtaining knowledge from a random forest
Some time ago I had to justify a RF model-fit to some chemists in my company. I spent quite time trying different visualization techniques. During the process, I accidentally also came up with some new techniques which I put into an R package (forestFloor) specifically for rando... | Obtaining knowledge from a random forest
Some time ago I had to justify a RF model-fit to some chemists in my company. I spent quite time trying different visualization techniques. During the process, I accidentally also came up with some ne |
1,154 | Obtaining knowledge from a random forest | To supplement these fine responses, I would mention use of gradient boosted trees (e.g. the GBM Package in R). In R, I prefer this to random forests because missing values are allowed as compared to randomForest where imputation is required. Variable importance and partial plots are available (as in randomForest) to ai... | Obtaining knowledge from a random forest | To supplement these fine responses, I would mention use of gradient boosted trees (e.g. the GBM Package in R). In R, I prefer this to random forests because missing values are allowed as compared to r | Obtaining knowledge from a random forest
To supplement these fine responses, I would mention use of gradient boosted trees (e.g. the GBM Package in R). In R, I prefer this to random forests because missing values are allowed as compared to randomForest where imputation is required. Variable importance and partial plots... | Obtaining knowledge from a random forest
To supplement these fine responses, I would mention use of gradient boosted trees (e.g. the GBM Package in R). In R, I prefer this to random forests because missing values are allowed as compared to r |
1,155 | Obtaining knowledge from a random forest | Late answer, but I came across a recent R package forestFloor (2015) that helps you doing this "unblackboxing" task in an automated fashion. It looks very promising!
library(forestFloor)
library(randomForest)
#simulate data
obs=1000
vars = 18
X = data.frame(replicate(vars,rnorm(obs)))
Y = with(X, X1^2 + sin(X2*pi) + 2 ... | Obtaining knowledge from a random forest | Late answer, but I came across a recent R package forestFloor (2015) that helps you doing this "unblackboxing" task in an automated fashion. It looks very promising!
library(forestFloor)
library(rando | Obtaining knowledge from a random forest
Late answer, but I came across a recent R package forestFloor (2015) that helps you doing this "unblackboxing" task in an automated fashion. It looks very promising!
library(forestFloor)
library(randomForest)
#simulate data
obs=1000
vars = 18
X = data.frame(replicate(vars,rnorm(... | Obtaining knowledge from a random forest
Late answer, but I came across a recent R package forestFloor (2015) that helps you doing this "unblackboxing" task in an automated fashion. It looks very promising!
library(forestFloor)
library(rando |
1,156 | Obtaining knowledge from a random forest | As mentioned by Zach, one way of understanding a model is to plot the response as the predictors vary. You can do this easily for "any" model with the plotmo R package. For example
library(randomForest)
data <- iris
data$Species <- factor(ifelse(data$Species=='virginica','virginica','other'))
mod <- randomForest(Spec... | Obtaining knowledge from a random forest | As mentioned by Zach, one way of understanding a model is to plot the response as the predictors vary. You can do this easily for "any" model with the plotmo R package. For example
library(randomFor | Obtaining knowledge from a random forest
As mentioned by Zach, one way of understanding a model is to plot the response as the predictors vary. You can do this easily for "any" model with the plotmo R package. For example
library(randomForest)
data <- iris
data$Species <- factor(ifelse(data$Species=='virginica','virg... | Obtaining knowledge from a random forest
As mentioned by Zach, one way of understanding a model is to plot the response as the predictors vary. You can do this easily for "any" model with the plotmo R package. For example
library(randomFor |
1,157 | Obtaining knowledge from a random forest | Late in the game but there are some new developments in this front, for example LIME and SHAP. Also a package worth checking is DALEX (in particular if using R but in any case contains nice cheatsheets etc.), though doesn't seem to cover interactions at the moment. And these are all model-agnostic so will work for rand... | Obtaining knowledge from a random forest | Late in the game but there are some new developments in this front, for example LIME and SHAP. Also a package worth checking is DALEX (in particular if using R but in any case contains nice cheatsheet | Obtaining knowledge from a random forest
Late in the game but there are some new developments in this front, for example LIME and SHAP. Also a package worth checking is DALEX (in particular if using R but in any case contains nice cheatsheets etc.), though doesn't seem to cover interactions at the moment. And these are... | Obtaining knowledge from a random forest
Late in the game but there are some new developments in this front, for example LIME and SHAP. Also a package worth checking is DALEX (in particular if using R but in any case contains nice cheatsheet |
1,158 | Obtaining knowledge from a random forest | I'm very interested in these types of questions myself. I do think there is a lot of information we can get out of a random forest.
About interactions, it seems like Breiman and Cutler have already tried to look at it, especially for classification RFs.
To my knowledge, this has not been implemented in the randomForest... | Obtaining knowledge from a random forest | I'm very interested in these types of questions myself. I do think there is a lot of information we can get out of a random forest.
About interactions, it seems like Breiman and Cutler have already tr | Obtaining knowledge from a random forest
I'm very interested in these types of questions myself. I do think there is a lot of information we can get out of a random forest.
About interactions, it seems like Breiman and Cutler have already tried to look at it, especially for classification RFs.
To my knowledge, this has... | Obtaining knowledge from a random forest
I'm very interested in these types of questions myself. I do think there is a lot of information we can get out of a random forest.
About interactions, it seems like Breiman and Cutler have already tr |
1,159 | Obtaining knowledge from a random forest | A slight modification of random forests that provide more information about the data are the recently-developed causal forest methods. See the GRF R-package and the motivating paper here. The idea is to use the random forest baseline methods to find heterogeneity in causal effects.
An earlier paper (here) gives a deta... | Obtaining knowledge from a random forest | A slight modification of random forests that provide more information about the data are the recently-developed causal forest methods. See the GRF R-package and the motivating paper here. The idea is | Obtaining knowledge from a random forest
A slight modification of random forests that provide more information about the data are the recently-developed causal forest methods. See the GRF R-package and the motivating paper here. The idea is to use the random forest baseline methods to find heterogeneity in causal effec... | Obtaining knowledge from a random forest
A slight modification of random forests that provide more information about the data are the recently-developed causal forest methods. See the GRF R-package and the motivating paper here. The idea is |
1,160 | Obtaining knowledge from a random forest | Late answer related to my question here (Can we make Random Forest 100% interpretable by fixing the seed?):
Let $z_1$ be the seed in the creation of boostrapped training set, and
$z_2 $ be the seed in the selection of feature's subset (for
simplification, I only list 2 kinds of seeds here).
From $z_1$, $m$ boostr... | Obtaining knowledge from a random forest | Late answer related to my question here (Can we make Random Forest 100% interpretable by fixing the seed?):
Let $z_1$ be the seed in the creation of boostrapped training set, and
$z_2 $ be the seed | Obtaining knowledge from a random forest
Late answer related to my question here (Can we make Random Forest 100% interpretable by fixing the seed?):
Let $z_1$ be the seed in the creation of boostrapped training set, and
$z_2 $ be the seed in the selection of feature's subset (for
simplification, I only list 2 kind... | Obtaining knowledge from a random forest
Late answer related to my question here (Can we make Random Forest 100% interpretable by fixing the seed?):
Let $z_1$ be the seed in the creation of boostrapped training set, and
$z_2 $ be the seed |
1,161 | What is the difference between linear regression and logistic regression? | Linear regression uses the general linear equation $Y=b_0+∑(b_i X_i)+\epsilon$ where $Y$ is a continuous dependent variable and independent variables $X_i$ are usually continuous (but can also be binary, e.g. when the linear model is used in a t-test) or other discrete domains. $\epsilon$ is a term for the variance tha... | What is the difference between linear regression and logistic regression? | Linear regression uses the general linear equation $Y=b_0+∑(b_i X_i)+\epsilon$ where $Y$ is a continuous dependent variable and independent variables $X_i$ are usually continuous (but can also be bina | What is the difference between linear regression and logistic regression?
Linear regression uses the general linear equation $Y=b_0+∑(b_i X_i)+\epsilon$ where $Y$ is a continuous dependent variable and independent variables $X_i$ are usually continuous (but can also be binary, e.g. when the linear model is used in a t-... | What is the difference between linear regression and logistic regression?
Linear regression uses the general linear equation $Y=b_0+∑(b_i X_i)+\epsilon$ where $Y$ is a continuous dependent variable and independent variables $X_i$ are usually continuous (but can also be bina |
1,162 | What is the difference between linear regression and logistic regression? | Linear Regression is used to establish a relationship between Dependent and Independent variables, which is useful in estimating the resultant dependent variable in case independent variable change. For example:
Using a Linear Regression, the relationship between Rain (R) and Umbrella Sales (U) is found to be - U =... | What is the difference between linear regression and logistic regression? | Linear Regression is used to establish a relationship between Dependent and Independent variables, which is useful in estimating the resultant dependent variable in case independent variable change. F | What is the difference between linear regression and logistic regression?
Linear Regression is used to establish a relationship between Dependent and Independent variables, which is useful in estimating the resultant dependent variable in case independent variable change. For example:
Using a Linear Regression, the... | What is the difference between linear regression and logistic regression?
Linear Regression is used to establish a relationship between Dependent and Independent variables, which is useful in estimating the resultant dependent variable in case independent variable change. F |
1,163 | What is the difference between linear regression and logistic regression? | The differences have been settled by DocBuckets and Pardis, but I want to add one way to compare their performance not mentioned.
Linear regression is usually solved by minimizing the least squares error of the model to the data, therefore large errors are penalized quadratically. Logistic regression is just the opposi... | What is the difference between linear regression and logistic regression? | The differences have been settled by DocBuckets and Pardis, but I want to add one way to compare their performance not mentioned.
Linear regression is usually solved by minimizing the least squares er | What is the difference between linear regression and logistic regression?
The differences have been settled by DocBuckets and Pardis, but I want to add one way to compare their performance not mentioned.
Linear regression is usually solved by minimizing the least squares error of the model to the data, therefore large ... | What is the difference between linear regression and logistic regression?
The differences have been settled by DocBuckets and Pardis, but I want to add one way to compare their performance not mentioned.
Linear regression is usually solved by minimizing the least squares er |
1,164 | What if residuals are normally distributed, but y is not? | It is reasonable for the residuals in a regression problem to be normally distributed, even though the response variable is not. Consider a univariate regression problem where $y \sim \mathcal{N}(\beta x, \sigma^2)$. so that the regression model is appropriate, and further assume that the true value of $\beta=1$. In ... | What if residuals are normally distributed, but y is not? | It is reasonable for the residuals in a regression problem to be normally distributed, even though the response variable is not. Consider a univariate regression problem where $y \sim \mathcal{N}(\be | What if residuals are normally distributed, but y is not?
It is reasonable for the residuals in a regression problem to be normally distributed, even though the response variable is not. Consider a univariate regression problem where $y \sim \mathcal{N}(\beta x, \sigma^2)$. so that the regression model is appropriate,... | What if residuals are normally distributed, but y is not?
It is reasonable for the residuals in a regression problem to be normally distributed, even though the response variable is not. Consider a univariate regression problem where $y \sim \mathcal{N}(\be |
1,165 | What if residuals are normally distributed, but y is not? | @DikranMarsupial is exactly right, of course, but it occurred to me that it might be nice to illustrate his point, especially since this concern seems to come up frequently. Specifically, the residuals of a regression model should be normally distributed for the p-values to be correct. However, even if the residuals ... | What if residuals are normally distributed, but y is not? | @DikranMarsupial is exactly right, of course, but it occurred to me that it might be nice to illustrate his point, especially since this concern seems to come up frequently. Specifically, the residua | What if residuals are normally distributed, but y is not?
@DikranMarsupial is exactly right, of course, but it occurred to me that it might be nice to illustrate his point, especially since this concern seems to come up frequently. Specifically, the residuals of a regression model should be normally distributed for th... | What if residuals are normally distributed, but y is not?
@DikranMarsupial is exactly right, of course, but it occurred to me that it might be nice to illustrate his point, especially since this concern seems to come up frequently. Specifically, the residua |
1,166 | What if residuals are normally distributed, but y is not? | In a regression model fitting, we should check for the normality of the response at each level of $X$, but not collectively as a whole since it's meaningless for this purpose. If you really need to check the normality of $Y$, then check it for each $X$ level. | What if residuals are normally distributed, but y is not? | In a regression model fitting, we should check for the normality of the response at each level of $X$, but not collectively as a whole since it's meaningless for this purpose. If you really need to ch | What if residuals are normally distributed, but y is not?
In a regression model fitting, we should check for the normality of the response at each level of $X$, but not collectively as a whole since it's meaningless for this purpose. If you really need to check the normality of $Y$, then check it for each $X$ level. | What if residuals are normally distributed, but y is not?
In a regression model fitting, we should check for the normality of the response at each level of $X$, but not collectively as a whole since it's meaningless for this purpose. If you really need to ch |
1,167 | Why does the Cauchy distribution have no mean? | You can mechanically check that the expected value does not exist, but this should be physically intuitive, at least if you accept Huygens' principle and the Law of Large Numbers. The conclusion of the Law of Large Numbers fails for a Cauchy distribution, so it can't have a mean. If you average $n$ independent Cauchy r... | Why does the Cauchy distribution have no mean? | You can mechanically check that the expected value does not exist, but this should be physically intuitive, at least if you accept Huygens' principle and the Law of Large Numbers. The conclusion of th | Why does the Cauchy distribution have no mean?
You can mechanically check that the expected value does not exist, but this should be physically intuitive, at least if you accept Huygens' principle and the Law of Large Numbers. The conclusion of the Law of Large Numbers fails for a Cauchy distribution, so it can't have ... | Why does the Cauchy distribution have no mean?
You can mechanically check that the expected value does not exist, but this should be physically intuitive, at least if you accept Huygens' principle and the Law of Large Numbers. The conclusion of th |
1,168 | Why does the Cauchy distribution have no mean? | Answer added in response to @whuber's comment on Michael Chernicks's answer
(and re-written completely to remove the error pointed out by whuber.)
The value of the integral for the expected value of a Cauchy random variable
is said to be undefined because the value can be "made" to be
anything one likes. The integral... | Why does the Cauchy distribution have no mean? | Answer added in response to @whuber's comment on Michael Chernicks's answer
(and re-written completely to remove the error pointed out by whuber.)
The value of the integral for the expected value of a | Why does the Cauchy distribution have no mean?
Answer added in response to @whuber's comment on Michael Chernicks's answer
(and re-written completely to remove the error pointed out by whuber.)
The value of the integral for the expected value of a Cauchy random variable
is said to be undefined because the value can be... | Why does the Cauchy distribution have no mean?
Answer added in response to @whuber's comment on Michael Chernicks's answer
(and re-written completely to remove the error pointed out by whuber.)
The value of the integral for the expected value of a |
1,169 | Why does the Cauchy distribution have no mean? | While the above answers are valid explanations of why the Cauchy distribution has no expectation, I find the fact that the ratio $X_1/X_2$ of two independent normal $\mathcal{N}(0,1)$ variates is Cauchy just as illuminating: indeed, we have
$$
\mathbb{E}\left[ \frac{|X_1|}{|X_2|} \right] =
\mathbb{E}\left[ |X_1| \right... | Why does the Cauchy distribution have no mean? | While the above answers are valid explanations of why the Cauchy distribution has no expectation, I find the fact that the ratio $X_1/X_2$ of two independent normal $\mathcal{N}(0,1)$ variates is Cauc | Why does the Cauchy distribution have no mean?
While the above answers are valid explanations of why the Cauchy distribution has no expectation, I find the fact that the ratio $X_1/X_2$ of two independent normal $\mathcal{N}(0,1)$ variates is Cauchy just as illuminating: indeed, we have
$$
\mathbb{E}\left[ \frac{|X_1|}... | Why does the Cauchy distribution have no mean?
While the above answers are valid explanations of why the Cauchy distribution has no expectation, I find the fact that the ratio $X_1/X_2$ of two independent normal $\mathcal{N}(0,1)$ variates is Cauc |
1,170 | Why does the Cauchy distribution have no mean? | The Cauchy has no mean because the point you select (0) is not a mean. It is a median and a mode. The mean for an absolutely continuous distribution is defined as $\int x f(x) dx$ where $f$ is the density function and the integral is taken over the domain of $f$ (which is $-\infty$ to $\infty$ in the case of the Cauc... | Why does the Cauchy distribution have no mean? | The Cauchy has no mean because the point you select (0) is not a mean. It is a median and a mode. The mean for an absolutely continuous distribution is defined as $\int x f(x) dx$ where $f$ is the d | Why does the Cauchy distribution have no mean?
The Cauchy has no mean because the point you select (0) is not a mean. It is a median and a mode. The mean for an absolutely continuous distribution is defined as $\int x f(x) dx$ where $f$ is the density function and the integral is taken over the domain of $f$ (which i... | Why does the Cauchy distribution have no mean?
The Cauchy has no mean because the point you select (0) is not a mean. It is a median and a mode. The mean for an absolutely continuous distribution is defined as $\int x f(x) dx$ where $f$ is the d |
1,171 | Why does the Cauchy distribution have no mean? | The Cauchy distribution is best thought of as the uniform distribution on a unit circle, so it would be surprising if averaging made sense. Suppose $f$ were some kind of "averaging function". That is, suppose that, for each finite subset $X$ of the unit circle, $f(X)$ was a point of the unit circle. Clearly, $f$ has to... | Why does the Cauchy distribution have no mean? | The Cauchy distribution is best thought of as the uniform distribution on a unit circle, so it would be surprising if averaging made sense. Suppose $f$ were some kind of "averaging function". That is, | Why does the Cauchy distribution have no mean?
The Cauchy distribution is best thought of as the uniform distribution on a unit circle, so it would be surprising if averaging made sense. Suppose $f$ were some kind of "averaging function". That is, suppose that, for each finite subset $X$ of the unit circle, $f(X)$ was ... | Why does the Cauchy distribution have no mean?
The Cauchy distribution is best thought of as the uniform distribution on a unit circle, so it would be surprising if averaging made sense. Suppose $f$ were some kind of "averaging function". That is, |
1,172 | Why does the Cauchy distribution have no mean? | The mean or expected value of some random variable $X$ is a Lebesgue integral defined over some probability measure $P$:
$$EX=\int XdP$$
The nonexistence of the mean of Cauchy random variable just means that the integral of Cauchy r.v. does not exist. This is because the tails of Cauchy distribution are heavy tails (c... | Why does the Cauchy distribution have no mean? | The mean or expected value of some random variable $X$ is a Lebesgue integral defined over some probability measure $P$:
$$EX=\int XdP$$
The nonexistence of the mean of Cauchy random variable just me | Why does the Cauchy distribution have no mean?
The mean or expected value of some random variable $X$ is a Lebesgue integral defined over some probability measure $P$:
$$EX=\int XdP$$
The nonexistence of the mean of Cauchy random variable just means that the integral of Cauchy r.v. does not exist. This is because the ... | Why does the Cauchy distribution have no mean?
The mean or expected value of some random variable $X$ is a Lebesgue integral defined over some probability measure $P$:
$$EX=\int XdP$$
The nonexistence of the mean of Cauchy random variable just me |
1,173 | Why does the Cauchy distribution have no mean? | Here is more of a visual explanation. (For those of us that are math challenged.). Take a cauchy distributed random number generator and try averaging the resulting values. Here is a good page on a function for this. https://math.stackexchange.com/questions/484395/how-to-generate-a-cauchy-random-variable
You will fi... | Why does the Cauchy distribution have no mean? | Here is more of a visual explanation. (For those of us that are math challenged.). Take a cauchy distributed random number generator and try averaging the resulting values. Here is a good page on a | Why does the Cauchy distribution have no mean?
Here is more of a visual explanation. (For those of us that are math challenged.). Take a cauchy distributed random number generator and try averaging the resulting values. Here is a good page on a function for this. https://math.stackexchange.com/questions/484395/how-t... | Why does the Cauchy distribution have no mean?
Here is more of a visual explanation. (For those of us that are math challenged.). Take a cauchy distributed random number generator and try averaging the resulting values. Here is a good page on a |
1,174 | Why does the Cauchy distribution have no mean? | Just to add to the excellent answers, I will make some comments about why the nonconvergence of the integral is relevant for statistical practice. As others have mentioned, if we allowed the principal value to be a "mean" then the slln are not anymore valid! Apart from this, think about the implications of the fact t... | Why does the Cauchy distribution have no mean? | Just to add to the excellent answers, I will make some comments about why the nonconvergence of the integral is relevant for statistical practice. As others have mentioned, if we allowed the principal | Why does the Cauchy distribution have no mean?
Just to add to the excellent answers, I will make some comments about why the nonconvergence of the integral is relevant for statistical practice. As others have mentioned, if we allowed the principal value to be a "mean" then the slln are not anymore valid! Apart from th... | Why does the Cauchy distribution have no mean?
Just to add to the excellent answers, I will make some comments about why the nonconvergence of the integral is relevant for statistical practice. As others have mentioned, if we allowed the principal |
1,175 | Why does the Cauchy distribution have no mean? | I wanted to be a bit picky for a second. The graphic at the top is wrong. The x-axis is in standard deviations, something that does not exist for the Cauchy distribution. I am being picky because I use the Cauchy distribution every single day of my life in my work. There is a practical case where the confusion coul... | Why does the Cauchy distribution have no mean? | I wanted to be a bit picky for a second. The graphic at the top is wrong. The x-axis is in standard deviations, something that does not exist for the Cauchy distribution. I am being picky because I | Why does the Cauchy distribution have no mean?
I wanted to be a bit picky for a second. The graphic at the top is wrong. The x-axis is in standard deviations, something that does not exist for the Cauchy distribution. I am being picky because I use the Cauchy distribution every single day of my life in my work. The... | Why does the Cauchy distribution have no mean?
I wanted to be a bit picky for a second. The graphic at the top is wrong. The x-axis is in standard deviations, something that does not exist for the Cauchy distribution. I am being picky because I |
1,176 | Why does the Cauchy distribution have no mean? | To put it simply, the area under the curve approaches infinity as you zoom out. If you sample a finite region, you can find a mean for that region. However, there is no mean for infinity. | Why does the Cauchy distribution have no mean? | To put it simply, the area under the curve approaches infinity as you zoom out. If you sample a finite region, you can find a mean for that region. However, there is no mean for infinity. | Why does the Cauchy distribution have no mean?
To put it simply, the area under the curve approaches infinity as you zoom out. If you sample a finite region, you can find a mean for that region. However, there is no mean for infinity. | Why does the Cauchy distribution have no mean?
To put it simply, the area under the curve approaches infinity as you zoom out. If you sample a finite region, you can find a mean for that region. However, there is no mean for infinity. |
1,177 | Books for self-studying time series analysis? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I would recommed the following books:
Time Series Ana... | Books for self-studying time series analysis? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Books for self-studying time series analysis?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I would ... | Books for self-studying time series analysis?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
1,178 | Books for self-studying time series analysis? | Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos is available free online.
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Wheelright is highly regarded, but this has the added advantage that you can see what you're getting for the price. | Books for self-studying time series analysis? | Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos is available free online.
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Whe | Books for self-studying time series analysis?
Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos is available free online.
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Wheelright is highly regarded, but this has the added advantage that you can ... | Books for self-studying time series analysis?
Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos is available free online.
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Whe |
1,179 | Books for self-studying time series analysis? | There are three books that I keep referring to always from an R programming and time series analysis perspective:
Time Series Analysis and Its Applications: With R Examples by
Shumway and Stoffer
Time Series Analysis: With Applications in R by Cryer and Chan.
Introductory Time Series with R by Cowpertwait and Metcalfe... | Books for self-studying time series analysis? | There are three books that I keep referring to always from an R programming and time series analysis perspective:
Time Series Analysis and Its Applications: With R Examples by
Shumway and Stoffer
Tim | Books for self-studying time series analysis?
There are three books that I keep referring to always from an R programming and time series analysis perspective:
Time Series Analysis and Its Applications: With R Examples by
Shumway and Stoffer
Time Series Analysis: With Applications in R by Cryer and Chan.
Introductory ... | Books for self-studying time series analysis?
There are three books that I keep referring to always from an R programming and time series analysis perspective:
Time Series Analysis and Its Applications: With R Examples by
Shumway and Stoffer
Tim |
1,180 | Books for self-studying time series analysis? | Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, interpretations, and best of all, you can download the data from the book's website and replicate the results for yours... | Books for self-studying time series analysis? | Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, | Books for self-studying time series analysis?
Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, interpretations, and best of all, you can download the data from the book... | Books for self-studying time series analysis?
Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, |
1,181 | Books for self-studying time series analysis? | It depends on how much math you want. For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded. | Books for self-studying time series analysis? | It depends on how much math you want. For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded. | Books for self-studying time series analysis?
It depends on how much math you want. For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded. | Books for self-studying time series analysis?
It depends on how much math you want. For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded. |
1,182 | Books for self-studying time series analysis? | Last year I started teaching introductory and semi-advanced time series course, so I embarked on journey of reading the (text-)books in the field to find suitable materials for students. Given that I did not find any post on CV, Quora or ResearchGate that would full satisfy me, I decided to share my conclusions here.
T... | Books for self-studying time series analysis? | Last year I started teaching introductory and semi-advanced time series course, so I embarked on journey of reading the (text-)books in the field to find suitable materials for students. Given that I | Books for self-studying time series analysis?
Last year I started teaching introductory and semi-advanced time series course, so I embarked on journey of reading the (text-)books in the field to find suitable materials for students. Given that I did not find any post on CV, Quora or ResearchGate that would full satisfy... | Books for self-studying time series analysis?
Last year I started teaching introductory and semi-advanced time series course, so I embarked on journey of reading the (text-)books in the field to find suitable materials for students. Given that I |
1,183 | Books for self-studying time series analysis? | In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an advanced econometrics text in the series,
Analysis of Integrated and Co-integrated Time Series with R.
I have not used t... | Books for self-studying time series analysis? | In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an | Books for self-studying time series analysis?
In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an advanced econometrics text in the series,
Analysis of Integrated and Co-in... | Books for self-studying time series analysis?
In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an |
1,184 | Books for self-studying time series analysis? | There are some good, free, online resources:
The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it's well written, has some good examples, and starts basically from scratch (ie. easy to get into).
Chapter 15, Statistic... | Books for self-studying time series analysis? | There are some good, free, online resources:
The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it | Books for self-studying time series analysis?
There are some good, free, online resources:
The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it's well written, has some good examples, and starts basically from scratch... | Books for self-studying time series analysis?
There are some good, free, online resources:
The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it |
1,185 | Books for self-studying time series analysis? | If you find Hamilton too difficult then there is Econometric Modeling: A Likelihood Approach (Princeton Uni Press) by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos than deeper theory. So if you're on a time constraint then that would be a good approach.
I would still recommend to per... | Books for self-studying time series analysis? | If you find Hamilton too difficult then there is Econometric Modeling: A Likelihood Approach (Princeton Uni Press) by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos | Books for self-studying time series analysis?
If you find Hamilton too difficult then there is Econometric Modeling: A Likelihood Approach (Princeton Uni Press) by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos than deeper theory. So if you're on a time constraint then that would be a... | Books for self-studying time series analysis?
If you find Hamilton too difficult then there is Econometric Modeling: A Likelihood Approach (Princeton Uni Press) by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos |
1,186 | Books for self-studying time series analysis? | In my opinion, you really can't beat Forecasting: principles and practice. It's written by CV's own Rob Hyndman and George Athanasopoulos, it's available for free online, and it's got tons of example code in R, making use of the excellent forecast package. | Books for self-studying time series analysis? | In my opinion, you really can't beat Forecasting: principles and practice. It's written by CV's own Rob Hyndman and George Athanasopoulos, it's available for free online, and it's got tons of examp | Books for self-studying time series analysis?
In my opinion, you really can't beat Forecasting: principles and practice. It's written by CV's own Rob Hyndman and George Athanasopoulos, it's available for free online, and it's got tons of example code in R, making use of the excellent forecast package. | Books for self-studying time series analysis?
In my opinion, you really can't beat Forecasting: principles and practice. It's written by CV's own Rob Hyndman and George Athanasopoulos, it's available for free online, and it's got tons of examp |
1,187 | Books for self-studying time series analysis? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
There's the NBER Summer Institute "What's New in Time ... | Books for self-studying time series analysis? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Books for self-studying time series analysis?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
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1,188 | Books for self-studying time series analysis? | Time Series Analysis: Univariate and Multivariate Methods by William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much higher price. It does not include R examples. It explicitely includes a great discussion/presentation of Intervention Detect... | Books for self-studying time series analysis? | Time Series Analysis: Univariate and Multivariate Methods by William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much high | Books for self-studying time series analysis?
Time Series Analysis: Univariate and Multivariate Methods by William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much higher price. It does not include R examples. It explicitely includes a great ... | Books for self-studying time series analysis?
Time Series Analysis: Univariate and Multivariate Methods by William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much high |
1,189 | Books for self-studying time series analysis? | If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory.
I think this book would complement Ender rather well.
The book opens with an intro to Stata language, followed by a quick review of regression and hypo... | Books for self-studying time series analysis? | If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory.
I think this book would complem | Books for self-studying time series analysis?
If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory.
I think this book would complement Ender rather well.
The book opens with an intro to Stata language, fol... | Books for self-studying time series analysis?
If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory.
I think this book would complem |
1,190 | Books for self-studying time series analysis? | There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Analysis" 1980 OR "Applied Time Series Analysis" by Richard McLeary. As you learn more about time series and decide that ... | Books for self-studying time series analysis? | There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Ana | Books for self-studying time series analysis?
There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Analysis" 1980 OR "Applied Time Series Analysis" by Richard McLeary. As you... | Books for self-studying time series analysis?
There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Ana |
1,191 | Books for self-studying time series analysis? | HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily grasped introductory econometrics with the book.
(2) There are supplemantary books to understand HILL's book:
a. Using EVie... | Books for self-studying time series analysis? | HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily gra | Books for self-studying time series analysis?
HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily grasped introductory econometrics with the book.
(2) There are supplemantary ... | Books for self-studying time series analysis?
HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily gra |
1,192 | Books for self-studying time series analysis? | I haven't seen anybody mention the book by Gloria Gonzalez-Rivera "Forecasting for Economics and Business". I have found it to be the best kept secret in the time series space. It is a terrific book. It will give you more intuition than Diebold, more context than Enders, and will actually be readable unlike Hamilton. W... | Books for self-studying time series analysis? | I haven't seen anybody mention the book by Gloria Gonzalez-Rivera "Forecasting for Economics and Business". I have found it to be the best kept secret in the time series space. It is a terrific book. | Books for self-studying time series analysis?
I haven't seen anybody mention the book by Gloria Gonzalez-Rivera "Forecasting for Economics and Business". I have found it to be the best kept secret in the time series space. It is a terrific book. It will give you more intuition than Diebold, more context than Enders, an... | Books for self-studying time series analysis?
I haven't seen anybody mention the book by Gloria Gonzalez-Rivera "Forecasting for Economics and Business". I have found it to be the best kept secret in the time series space. It is a terrific book. |
1,193 | Books for self-studying time series analysis? | Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) is quite up to date and offers a clear exposition. | Books for self-studying time series analysis? | Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) is quite up to date and offers a clear exposition. | Books for self-studying time series analysis?
Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) is quite up to date and offers a clear exposition. | Books for self-studying time series analysis?
Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) is quite up to date and offers a clear exposition. |
1,194 | Books for self-studying time series analysis? | I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor the Hamilton work and many others mentioned. There is a a huge gap between academic and practitioner audiences in this t... | Books for self-studying time series analysis? | I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor | Books for self-studying time series analysis?
I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor the Hamilton work and many others mentioned. There is a a huge gap between... | Books for self-studying time series analysis?
I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor |
1,195 | Books for self-studying time series analysis? | I will recommend you a textbook related with time series analysis. I read this book and got the idea. This book is very easy to understand.
The link for the book :https://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html
This book is very good because it shows everything from scratch. This... | Books for self-studying time series analysis? | I will recommend you a textbook related with time series analysis. I read this book and got the idea. This book is very easy to understand.
The link for the book :https://a-little-book-of-r-for-time-s | Books for self-studying time series analysis?
I will recommend you a textbook related with time series analysis. I read this book and got the idea. This book is very easy to understand.
The link for the book :https://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html
This book is very good ... | Books for self-studying time series analysis?
I will recommend you a textbook related with time series analysis. I read this book and got the idea. This book is very easy to understand.
The link for the book :https://a-little-book-of-r-for-time-s |
1,196 | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? | Autoencoder is a simple 3-layer neural network where output units are directly connected back to input units. E.g. in a network like this:
output[i] has edge back to input[i] for every i. Typically, number of hidden units is much less then number of visible (input/output) ones. As a result, when you pass data through ... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut | Autoencoder is a simple 3-layer neural network where output units are directly connected back to input units. E.g. in a network like this:
output[i] has edge back to input[i] for every i. Typically, | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?
Autoencoder is a simple 3-layer neural network where output units are directly connected back to input units. E.g. in a network like this:
output[i] has edge back to input[i] for every i. Typically, number o... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut
Autoencoder is a simple 3-layer neural network where output units are directly connected back to input units. E.g. in a network like this:
output[i] has edge back to input[i] for every i. Typically, |
1,197 | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? | All of these architectures can be interpreted as a neural network. The main difference between AutoEncoder and Convolutional Network is the level of network hardwiring. Convolutional Nets are pretty much hardwired. Convolution operation is pretty much local in image domain, meaning much more sparsity in the number of c... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut | All of these architectures can be interpreted as a neural network. The main difference between AutoEncoder and Convolutional Network is the level of network hardwiring. Convolutional Nets are pretty m | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?
All of these architectures can be interpreted as a neural network. The main difference between AutoEncoder and Convolutional Network is the level of network hardwiring. Convolutional Nets are pretty much hard... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut
All of these architectures can be interpreted as a neural network. The main difference between AutoEncoder and Convolutional Network is the level of network hardwiring. Convolutional Nets are pretty m |
1,198 | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? | RBMs can be seen as some kind of probabilistic auto encoder. Actually, it has been shown that under certain conditions they become equivalent.
Nevertheless, it is much harder to show this equivalency than to just believe they are different beasts. Indeed, I find it hard to find a lot of similarities among the three, as... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut | RBMs can be seen as some kind of probabilistic auto encoder. Actually, it has been shown that under certain conditions they become equivalent.
Nevertheless, it is much harder to show this equivalency | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?
RBMs can be seen as some kind of probabilistic auto encoder. Actually, it has been shown that under certain conditions they become equivalent.
Nevertheless, it is much harder to show this equivalency than to ... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut
RBMs can be seen as some kind of probabilistic auto encoder. Actually, it has been shown that under certain conditions they become equivalent.
Nevertheless, it is much harder to show this equivalency |
1,199 | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? | I can't tell you much about RBMs, but autoencoders and CNNs are two different kinds of things. An autoencoder is a neural network that is trained in an unsupervised fashion. The goal of an autoencoder is to find a more compact representation of the data by learning an encoder, which transforms the data to their corresp... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut | I can't tell you much about RBMs, but autoencoders and CNNs are two different kinds of things. An autoencoder is a neural network that is trained in an unsupervised fashion. The goal of an autoencoder | What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?
I can't tell you much about RBMs, but autoencoders and CNNs are two different kinds of things. An autoencoder is a neural network that is trained in an unsupervised fashion. The goal of an autoencoder is to f... | What is the difference between convolutional neural networks, restricted Boltzmann machines, and aut
I can't tell you much about RBMs, but autoencoders and CNNs are two different kinds of things. An autoencoder is a neural network that is trained in an unsupervised fashion. The goal of an autoencoder |
1,200 | Nested cross validation for model selection | How do I choose a model from this [outer cross validation] output?
Short answer: You don't.
Treat the inner cross validation as part of the model fitting procedure. That means that the fitting including the fitting of the hyper-parameters (this is where the inner cross validation hides) is just like any other model... | Nested cross validation for model selection | How do I choose a model from this [outer cross validation] output?
Short answer: You don't.
Treat the inner cross validation as part of the model fitting procedure. That means that the fitting inc | Nested cross validation for model selection
How do I choose a model from this [outer cross validation] output?
Short answer: You don't.
Treat the inner cross validation as part of the model fitting procedure. That means that the fitting including the fitting of the hyper-parameters (this is where the inner cross va... | Nested cross validation for model selection
How do I choose a model from this [outer cross validation] output?
Short answer: You don't.
Treat the inner cross validation as part of the model fitting procedure. That means that the fitting inc |
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