idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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2,701 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | It's wrong in part because it's based on a mathematical fallacy. (It's even more wrong because it's such blatant voter-suppression propaganda, but that's not a suitable topic for discussion here.)
The implicit context is one in which an election looks like it's on the fence. One reasonable model is that there will be... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | It's wrong in part because it's based on a mathematical fallacy. (It's even more wrong because it's such blatant voter-suppression propaganda, but that's not a suitable topic for discussion here.)
Th | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
It's wrong in part because it's based on a mathematical fallacy. (It's even more wrong because it's such blatant voter-suppression propaganda, but that's not a suitable topic for discussion here.)
The implicit context is one in... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
It's wrong in part because it's based on a mathematical fallacy. (It's even more wrong because it's such blatant voter-suppression propaganda, but that's not a suitable topic for discussion here.)
Th |
2,702 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | I must disappoint you: current economic theory cannot explain why people keep showing up in elections, because it appears to be irrational. See a survey of literature on this subject on pages 16-35 of Geys, Benny (2006) - "‘Rational’ Theories of Voter Turnout: A Review". The voter turnout is a percentage of voters tha... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | I must disappoint you: current economic theory cannot explain why people keep showing up in elections, because it appears to be irrational. See a survey of literature on this subject on pages 16-35 of | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
I must disappoint you: current economic theory cannot explain why people keep showing up in elections, because it appears to be irrational. See a survey of literature on this subject on pages 16-35 of Geys, Benny (2006) - "‘Rat... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
I must disappoint you: current economic theory cannot explain why people keep showing up in elections, because it appears to be irrational. See a survey of literature on this subject on pages 16-35 of |
2,703 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | I'm going to take a different tack than the other answers, and argue both sides of the question.
First, let's show that voting is a pointless waste of time.
The function of an election is to derive a single outcome, called "the will of the electorate", from many samples of the individual wills of individual electors. ... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | I'm going to take a different tack than the other answers, and argue both sides of the question.
First, let's show that voting is a pointless waste of time.
The function of an election is to derive a | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
I'm going to take a different tack than the other answers, and argue both sides of the question.
First, let's show that voting is a pointless waste of time.
The function of an election is to derive a single outcome, called "the ... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
I'm going to take a different tack than the other answers, and argue both sides of the question.
First, let's show that voting is a pointless waste of time.
The function of an election is to derive a |
2,704 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | The analysis presented in whuber's answer reflects the Penrose square root law, which states that, under certain assumptions, the probability that a given vote is decisive scales like $1/\sqrt{N}$. The assumptions underlying that analysis, however, are too strong to be realistic in most real-world scenarios. In particu... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | The analysis presented in whuber's answer reflects the Penrose square root law, which states that, under certain assumptions, the probability that a given vote is decisive scales like $1/\sqrt{N}$. Th | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
The analysis presented in whuber's answer reflects the Penrose square root law, which states that, under certain assumptions, the probability that a given vote is decisive scales like $1/\sqrt{N}$. The assumptions underlying tha... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
The analysis presented in whuber's answer reflects the Penrose square root law, which states that, under certain assumptions, the probability that a given vote is decisive scales like $1/\sqrt{N}$. Th |
2,705 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | It is easy to construct situations, where voting matters, e.g. the population consists of 3 people (including myself), one votes red, one votes blue, then clearly my vote matters.
Of course in your quote, not such trivial quotes are meant, but real-life situations with maybe millions of voters.
So let us extend my tr... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | It is easy to construct situations, where voting matters, e.g. the population consists of 3 people (including myself), one votes red, one votes blue, then clearly my vote matters.
Of course in your q | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
It is easy to construct situations, where voting matters, e.g. the population consists of 3 people (including myself), one votes red, one votes blue, then clearly my vote matters.
Of course in your quote, not such trivial quote... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
It is easy to construct situations, where voting matters, e.g. the population consists of 3 people (including myself), one votes red, one votes blue, then clearly my vote matters.
Of course in your q |
2,706 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | You can consider the probability that the voting result is a tie when there are an even number of total voters (in which case the vote of an individual matters). We consider for simplicity even values of $n$ but this can be extended to odd values of $n$.
Assumption case 1
Let's consider the vote $X_i$ of each voter $i... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | You can consider the probability that the voting result is a tie when there are an even number of total voters (in which case the vote of an individual matters). We consider for simplicity even values | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
You can consider the probability that the voting result is a tie when there are an even number of total voters (in which case the vote of an individual matters). We consider for simplicity even values of $n$ but this can be exte... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
You can consider the probability that the voting result is a tie when there are an even number of total voters (in which case the vote of an individual matters). We consider for simplicity even values |
2,707 | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | A simple model. New captain has to be chosen on a ship. There are 6 voters. Two candidates agreed to compete for the office - audacious Mr. Zero and brilliant Mr. One. Nobody on the deck is obliged to vote. We don't know how many voters will take part in the election.
Simulation
The number of voters participating in t... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning? | A simple model. New captain has to be chosen on a ship. There are 6 voters. Two candidates agreed to compete for the office - audacious Mr. Zero and brilliant Mr. One. Nobody on the deck is obliged to | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
A simple model. New captain has to be chosen on a ship. There are 6 voters. Two candidates agreed to compete for the office - audacious Mr. Zero and brilliant Mr. One. Nobody on the deck is obliged to vote. We don't know how man... | Do not vote, one vote will not reverse election results. What is wrong with this reasoning?
A simple model. New captain has to be chosen on a ship. There are 6 voters. Two candidates agreed to compete for the office - audacious Mr. Zero and brilliant Mr. One. Nobody on the deck is obliged to |
2,708 | How to interpret F-measure values? | I cannot think of an intuitive meaning of the F measure, because it's just a combined metric. What's more intuitive than F-mesure, of course, is precision and recall.
But using two values, we often cannot determine if one algorithm is superior to another. For example, if one algorithm has higher precision but lower re... | How to interpret F-measure values? | I cannot think of an intuitive meaning of the F measure, because it's just a combined metric. What's more intuitive than F-mesure, of course, is precision and recall.
But using two values, we often c | How to interpret F-measure values?
I cannot think of an intuitive meaning of the F measure, because it's just a combined metric. What's more intuitive than F-mesure, of course, is precision and recall.
But using two values, we often cannot determine if one algorithm is superior to another. For example, if one algorith... | How to interpret F-measure values?
I cannot think of an intuitive meaning of the F measure, because it's just a combined metric. What's more intuitive than F-mesure, of course, is precision and recall.
But using two values, we often c |
2,709 | How to interpret F-measure values? | The importance of the F1 score differs based on the distribution of the target variable. Lets assume the target variable is a binary label.
Balanced class: In this situation, the F1 score can effectively be ignored, the mis-classification rate is key.
Unbalanced class, but both classes are important: If the class dist... | How to interpret F-measure values? | The importance of the F1 score differs based on the distribution of the target variable. Lets assume the target variable is a binary label.
Balanced class: In this situation, the F1 score can effecti | How to interpret F-measure values?
The importance of the F1 score differs based on the distribution of the target variable. Lets assume the target variable is a binary label.
Balanced class: In this situation, the F1 score can effectively be ignored, the mis-classification rate is key.
Unbalanced class, but both class... | How to interpret F-measure values?
The importance of the F1 score differs based on the distribution of the target variable. Lets assume the target variable is a binary label.
Balanced class: In this situation, the F1 score can effecti |
2,710 | How to interpret F-measure values? | F-measure has an intuitive meaning. It tells you how precise your classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of instances).
With high precision but low recall, you classifier is extremely accurate, but it misses a significant number of... | How to interpret F-measure values? | F-measure has an intuitive meaning. It tells you how precise your classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of inst | How to interpret F-measure values?
F-measure has an intuitive meaning. It tells you how precise your classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of instances).
With high precision but low recall, you classifier is extremely accurate, bu... | How to interpret F-measure values?
F-measure has an intuitive meaning. It tells you how precise your classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of inst |
2,711 | How to interpret F-measure values? | The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the other, the harmonic mean quickly decreases. It is greatest however, when both precision and recall are equal.
Given F-m... | How to interpret F-measure values? | The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the | How to interpret F-measure values?
The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the other, the harmonic mean quickly decreases. It is greatest however, when both precis... | How to interpret F-measure values?
The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the |
2,712 | How to interpret F-measure values? | With precision on the y-axis and recall on the x-axis, the slope of the level curve $F_{\beta}$ at (1, 1) is $-1/\beta^2$.
Given $$P = \frac{TP}{TP+FP}$$ and $$R = \frac{TP}{TP+FN}$$, let $\alpha$ be the ratio of the cost of false negatives to false positives. Then total cost of error is proportional to $$\alpha \frac{... | How to interpret F-measure values? | With precision on the y-axis and recall on the x-axis, the slope of the level curve $F_{\beta}$ at (1, 1) is $-1/\beta^2$.
Given $$P = \frac{TP}{TP+FP}$$ and $$R = \frac{TP}{TP+FN}$$, let $\alpha$ be | How to interpret F-measure values?
With precision on the y-axis and recall on the x-axis, the slope of the level curve $F_{\beta}$ at (1, 1) is $-1/\beta^2$.
Given $$P = \frac{TP}{TP+FP}$$ and $$R = \frac{TP}{TP+FN}$$, let $\alpha$ be the ratio of the cost of false negatives to false positives. Then total cost of error... | How to interpret F-measure values?
With precision on the y-axis and recall on the x-axis, the slope of the level curve $F_{\beta}$ at (1, 1) is $-1/\beta^2$.
Given $$P = \frac{TP}{TP+FP}$$ and $$R = \frac{TP}{TP+FN}$$, let $\alpha$ be |
2,713 | How to interpret F-measure values? | I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which [the authors] call $F^*$ (F-star), which has an immediate practical interpretation." It even cited this very discussion on Cross Validated.
Specifically, $F^* = F/(2-F)$ "is the proportion of the... | How to interpret F-measure values? | I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which [the authors] call $F^*$ (F-star), which has an immediate practical interpr | How to interpret F-measure values?
I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which [the authors] call $F^*$ (F-star), which has an immediate practical interpretation." It even cited this very discussion on Cross Validated.
Specifically, $F^* =... | How to interpret F-measure values?
I just want to note the following paper, published this year, that proposes "a simple transformation of the F-measure, which [the authors] call $F^*$ (F-star), which has an immediate practical interpr |
2,714 | How to interpret F-measure values? | The formula for F-measure (F1, with beta=1) is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics (forgetting about the factor 2).
This could give you a possible interpretation, and you can think about both electronic or thermal resistances. This analogy w... | How to interpret F-measure values? | The formula for F-measure (F1, with beta=1) is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics (forgetting about the factor 2).
This | How to interpret F-measure values?
The formula for F-measure (F1, with beta=1) is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics (forgetting about the factor 2).
This could give you a possible interpretation, and you can think about both electronic or ... | How to interpret F-measure values?
The formula for F-measure (F1, with beta=1) is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics (forgetting about the factor 2).
This |
2,715 | How to interpret F-measure values? | The closest intuitive meaning of the f1-score is being perceived as the mean of the recall and the precision. Let's clear it for you :
In a classification task, you may be planning to build a classifier with high precision AND recall. For example, a classifier that tells if a person is honest or not.
For precision, y... | How to interpret F-measure values? | The closest intuitive meaning of the f1-score is being perceived as the mean of the recall and the precision. Let's clear it for you :
In a classification task, you may be planning to build a classif | How to interpret F-measure values?
The closest intuitive meaning of the f1-score is being perceived as the mean of the recall and the precision. Let's clear it for you :
In a classification task, you may be planning to build a classifier with high precision AND recall. For example, a classifier that tells if a person ... | How to interpret F-measure values?
The closest intuitive meaning of the f1-score is being perceived as the mean of the recall and the precision. Let's clear it for you :
In a classification task, you may be planning to build a classif |
2,716 | How to interpret F-measure values? | you can write the F-measure equation http://e.hiphotos.baidu.com/baike/s%3D118/sign=e8083e4396dda144de0968b38ab6d009/f2deb48f8c5494ee14c095492cf5e0fe98257e84.jpg
in another way, like
$$F_\beta=1/((\beta^2/(\beta^2+1))1/r+(1/(\beta^2+1))1/p)$$
so, when $β^2<1$, $p$ should be more important (or, larger, to get ... | How to interpret F-measure values? | you can write the F-measure equation http://e.hiphotos.baidu.com/baike/s%3D118/sign=e8083e4396dda144de0968b38ab6d009/f2deb48f8c5494ee14c095492cf5e0fe98257e84.jpg
in another way, like
$$F_\be | How to interpret F-measure values?
you can write the F-measure equation http://e.hiphotos.baidu.com/baike/s%3D118/sign=e8083e4396dda144de0968b38ab6d009/f2deb48f8c5494ee14c095492cf5e0fe98257e84.jpg
in another way, like
$$F_\beta=1/((\beta^2/(\beta^2+1))1/r+(1/(\beta^2+1))1/p)$$
so, when $β^2<1$, $p$ should be ... | How to interpret F-measure values?
you can write the F-measure equation http://e.hiphotos.baidu.com/baike/s%3D118/sign=e8083e4396dda144de0968b38ab6d009/f2deb48f8c5494ee14c095492cf5e0fe98257e84.jpg
in another way, like
$$F_\be |
2,717 | How to interpret F-measure values? | Knowing that F1 score is harmonic mean of precision and recall, below is a little brief about them.
I would say Recall is more about false negatives
.i.e, Having a higher Recall means there are less FALSE NEGATIVES.
$$\text{Recall}=\frac{tp}{tp+fn}$$
As much as less FN or Zero FN means, your model prediction is really ... | How to interpret F-measure values? | Knowing that F1 score is harmonic mean of precision and recall, below is a little brief about them.
I would say Recall is more about false negatives
.i.e, Having a higher Recall means there are less F | How to interpret F-measure values?
Knowing that F1 score is harmonic mean of precision and recall, below is a little brief about them.
I would say Recall is more about false negatives
.i.e, Having a higher Recall means there are less FALSE NEGATIVES.
$$\text{Recall}=\frac{tp}{tp+fn}$$
As much as less FN or Zero FN mean... | How to interpret F-measure values?
Knowing that F1 score is harmonic mean of precision and recall, below is a little brief about them.
I would say Recall is more about false negatives
.i.e, Having a higher Recall means there are less F |
2,718 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Yes there is. Generally it is termed base rate fallacy or more specific false positive paradox. There is even a wikipedia article about it: see here | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Yes there is. Generally it is termed base rate fallacy or more specific false positive paradox. There is even a wikipedia article about it: see here | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Yes there is. Generally it is termed base rate fallacy or more specific false positive paradox. There is even a wikipedia article about it: see here | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Yes there is. Generally it is termed base rate fallacy or more specific false positive paradox. There is even a wikipedia article about it: see here |
2,719 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Unfortunately I have no name for this fallacy. When I need to explain this I have found it usefull to refer to diseases that are commonly known amongst laypersons but are ridiculously rare.
I live in Germany and whilst everyone has read about the plague in their history books, everyone knows that as a German doctor I ... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Unfortunately I have no name for this fallacy. When I need to explain this I have found it usefull to refer to diseases that are commonly known amongst laypersons but are ridiculously rare.
I live in | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Unfortunately I have no name for this fallacy. When I need to explain this I have found it usefull to refer to diseases that are commonly known amongst laypersons but are ridiculously rare.
I live in Germany and whilst... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Unfortunately I have no name for this fallacy. When I need to explain this I have found it usefull to refer to diseases that are commonly known amongst laypersons but are ridiculously rare.
I live in |
2,720 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | The base rate fallacy has to do with specialization to different populations, which does not capture a broader misconception that high accuracy implies both low false positive and low false negative rates.
In addressing the conundrum of high accuracy with a high false positive rate, I find it impossible to go beyond v... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | The base rate fallacy has to do with specialization to different populations, which does not capture a broader misconception that high accuracy implies both low false positive and low false negative r | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
The base rate fallacy has to do with specialization to different populations, which does not capture a broader misconception that high accuracy implies both low false positive and low false negative rates.
In addressin... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
The base rate fallacy has to do with specialization to different populations, which does not capture a broader misconception that high accuracy implies both low false positive and low false negative r |
2,721 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Just draw yourself a simple decision tree, and it becomes obvious. See attached. I can also send an ultra simple spreadsheet that illustrates the impact precisely. | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Just draw yourself a simple decision tree, and it becomes obvious. See attached. I can also send an ultra simple spreadsheet that illustrates the impact precisely. | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Just draw yourself a simple decision tree, and it becomes obvious. See attached. I can also send an ultra simple spreadsheet that illustrates the impact precisely. | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Just draw yourself a simple decision tree, and it becomes obvious. See attached. I can also send an ultra simple spreadsheet that illustrates the impact precisely. |
2,722 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Late to the game, but here are some things others haven't mentioned.
1) Firstly there is a statistic called Kappa or Cohen's Kappa which measures how much a method improves over random guessing. For a test with two outcomes, random guessing is just guessing the majority class. For example if a disease is carried by 1... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Late to the game, but here are some things others haven't mentioned.
1) Firstly there is a statistic called Kappa or Cohen's Kappa which measures how much a method improves over random guessing. For a | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Late to the game, but here are some things others haven't mentioned.
1) Firstly there is a statistic called Kappa or Cohen's Kappa which measures how much a method improves over random guessing. For a test with two outc... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Late to the game, but here are some things others haven't mentioned.
1) Firstly there is a statistic called Kappa or Cohen's Kappa which measures how much a method improves over random guessing. For a |
2,723 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | As is true of many questions and answers, it depends...
In the case of cancer screening (mammogram, colonoscopy, etc.) and many other screening tests for a disease or condition, this is almost always the case. For a screening test to have some value, it must be "sensitive" enough to detect the relatively rare cases (sa... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | As is true of many questions and answers, it depends...
In the case of cancer screening (mammogram, colonoscopy, etc.) and many other screening tests for a disease or condition, this is almost always | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
As is true of many questions and answers, it depends...
In the case of cancer screening (mammogram, colonoscopy, etc.) and many other screening tests for a disease or condition, this is almost always the case. For a scr... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
As is true of many questions and answers, it depends...
In the case of cancer screening (mammogram, colonoscopy, etc.) and many other screening tests for a disease or condition, this is almost always |
2,724 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Look at this shiny app tool https://kennis-research.shinyapps.io/Bayes-App/ that explains the relationship between sensitivity, specificity and prevalence. In essence, the ability of the test to discover true positives is a function of both the effectiveness of the test (sensitivity and specificity) and the prevalence ... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Look at this shiny app tool https://kennis-research.shinyapps.io/Bayes-App/ that explains the relationship between sensitivity, specificity and prevalence. In essence, the ability of the test to disco | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Look at this shiny app tool https://kennis-research.shinyapps.io/Bayes-App/ that explains the relationship between sensitivity, specificity and prevalence. In essence, the ability of the test to discover true positives ... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Look at this shiny app tool https://kennis-research.shinyapps.io/Bayes-App/ that explains the relationship between sensitivity, specificity and prevalence. In essence, the ability of the test to disco |
2,725 | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Use The KISS method to explain it to everyone... Keep It Simple Stupid K.I.S.S. .
In accounting a simple audit starts with a 1% sample of total transactions for a specific expenditure(s) or income(s) vs actual bank deposits & withdrawals. If they don't match or "add" up. You increase the sample size up to 5%. The more... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives | Use The KISS method to explain it to everyone... Keep It Simple Stupid K.I.S.S. .
In accounting a simple audit starts with a 1% sample of total transactions for a specific expenditure(s) or income(s) | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Use The KISS method to explain it to everyone... Keep It Simple Stupid K.I.S.S. .
In accounting a simple audit starts with a 1% sample of total transactions for a specific expenditure(s) or income(s) vs actual bank depo... | Is there a name for the phenomenon of false positives counterintuitively outstripping true positives
Use The KISS method to explain it to everyone... Keep It Simple Stupid K.I.S.S. .
In accounting a simple audit starts with a 1% sample of total transactions for a specific expenditure(s) or income(s) |
2,726 | How and why do normalization and feature scaling work? | It's simply a case of getting all your data on the same scale: if the scales for different features are wildly different, this can have a knock-on effect on your ability to learn (depending on what methods you're using to do it). Ensuring standardised feature values implicitly weights all features equally in their repr... | How and why do normalization and feature scaling work? | It's simply a case of getting all your data on the same scale: if the scales for different features are wildly different, this can have a knock-on effect on your ability to learn (depending on what me | How and why do normalization and feature scaling work?
It's simply a case of getting all your data on the same scale: if the scales for different features are wildly different, this can have a knock-on effect on your ability to learn (depending on what methods you're using to do it). Ensuring standardised feature value... | How and why do normalization and feature scaling work?
It's simply a case of getting all your data on the same scale: if the scales for different features are wildly different, this can have a knock-on effect on your ability to learn (depending on what me |
2,727 | How and why do normalization and feature scaling work? | It is true that preprocessing in machine learning is somewhat a very black art. It is not written down in papers a lot why several preprocessing steps are essential to make it work. I am also not sure if it is understood in every case. To make things more complicated, it depends heavily on the method you use and also o... | How and why do normalization and feature scaling work? | It is true that preprocessing in machine learning is somewhat a very black art. It is not written down in papers a lot why several preprocessing steps are essential to make it work. I am also not sure | How and why do normalization and feature scaling work?
It is true that preprocessing in machine learning is somewhat a very black art. It is not written down in papers a lot why several preprocessing steps are essential to make it work. I am also not sure if it is understood in every case. To make things more complicat... | How and why do normalization and feature scaling work?
It is true that preprocessing in machine learning is somewhat a very black art. It is not written down in papers a lot why several preprocessing steps are essential to make it work. I am also not sure |
2,728 | How and why do normalization and feature scaling work? | Some ideas, references and plots on why input normalization can be useful for ANN and k-means:
K-means:
K-means clustering is "isotropic" in all directions of space and
therefore tends to produce more or less round (rather than elongated)
clusters. In this situation leaving variances unequal is equivalent to
put... | How and why do normalization and feature scaling work? | Some ideas, references and plots on why input normalization can be useful for ANN and k-means:
K-means:
K-means clustering is "isotropic" in all directions of space and
therefore tends to produce m | How and why do normalization and feature scaling work?
Some ideas, references and plots on why input normalization can be useful for ANN and k-means:
K-means:
K-means clustering is "isotropic" in all directions of space and
therefore tends to produce more or less round (rather than elongated)
clusters. In this sit... | How and why do normalization and feature scaling work?
Some ideas, references and plots on why input normalization can be useful for ANN and k-means:
K-means:
K-means clustering is "isotropic" in all directions of space and
therefore tends to produce m |
2,729 | How and why do normalization and feature scaling work? | There are two separate issues:
a) learning the right function
eg k-means: the input scale basically specifies the similarity, so the clusters found depend on the scaling.
regularisation - eg l2 weights regularisation - you assume each weight should be "equally small"- if your data are not scaled "appropriately" this w... | How and why do normalization and feature scaling work? | There are two separate issues:
a) learning the right function
eg k-means: the input scale basically specifies the similarity, so the clusters found depend on the scaling.
regularisation - eg l2 weigh | How and why do normalization and feature scaling work?
There are two separate issues:
a) learning the right function
eg k-means: the input scale basically specifies the similarity, so the clusters found depend on the scaling.
regularisation - eg l2 weights regularisation - you assume each weight should be "equally sma... | How and why do normalization and feature scaling work?
There are two separate issues:
a) learning the right function
eg k-means: the input scale basically specifies the similarity, so the clusters found depend on the scaling.
regularisation - eg l2 weigh |
2,730 | How and why do normalization and feature scaling work? | Why does feature scaling work? I can give you an example (from Quora)
Let me answer this from general ML perspective and not only neural networks. When you collect data and extract features, many times the data is collected on different scales. For example, the age of employees in a company may be between 21-70 years,... | How and why do normalization and feature scaling work? | Why does feature scaling work? I can give you an example (from Quora)
Let me answer this from general ML perspective and not only neural networks. When you collect data and extract features, many tim | How and why do normalization and feature scaling work?
Why does feature scaling work? I can give you an example (from Quora)
Let me answer this from general ML perspective and not only neural networks. When you collect data and extract features, many times the data is collected on different scales. For example, the ag... | How and why do normalization and feature scaling work?
Why does feature scaling work? I can give you an example (from Quora)
Let me answer this from general ML perspective and not only neural networks. When you collect data and extract features, many tim |
2,731 | How and why do normalization and feature scaling work? | Pre-processing often works because it does remove features of the data which are not related to the classification problem you are trying solve. Think for instance about classifying sound data from different speakers. Fluctuations in loudness (amplitude) might be irrelevant, whereas the frequency spectrum is the really... | How and why do normalization and feature scaling work? | Pre-processing often works because it does remove features of the data which are not related to the classification problem you are trying solve. Think for instance about classifying sound data from di | How and why do normalization and feature scaling work?
Pre-processing often works because it does remove features of the data which are not related to the classification problem you are trying solve. Think for instance about classifying sound data from different speakers. Fluctuations in loudness (amplitude) might be i... | How and why do normalization and feature scaling work?
Pre-processing often works because it does remove features of the data which are not related to the classification problem you are trying solve. Think for instance about classifying sound data from di |
2,732 | How and why do normalization and feature scaling work? | This paper is talks only about k-means, but it explains and proves the requirement of data preprocessing quite nicely.
Standardization is the central
preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a
specific range. In this paper, we have analyz... | How and why do normalization and feature scaling work? | This paper is talks only about k-means, but it explains and proves the requirement of data preprocessing quite nicely.
Standardization is the central
preprocessing step in data mining, to standard | How and why do normalization and feature scaling work?
This paper is talks only about k-means, but it explains and proves the requirement of data preprocessing quite nicely.
Standardization is the central
preprocessing step in data mining, to standardize values of features or attributes from different dynamic range... | How and why do normalization and feature scaling work?
This paper is talks only about k-means, but it explains and proves the requirement of data preprocessing quite nicely.
Standardization is the central
preprocessing step in data mining, to standard |
2,733 | How and why do normalization and feature scaling work? | I think that this is done simply so that the feature with a larger value does not overshadow the effects of the feature with a smaller value when learning a classifier . This becomes particularly important if the feature with smaller values actually contributes to class separability .The classifiers like logistic regre... | How and why do normalization and feature scaling work? | I think that this is done simply so that the feature with a larger value does not overshadow the effects of the feature with a smaller value when learning a classifier . This becomes particularly impo | How and why do normalization and feature scaling work?
I think that this is done simply so that the feature with a larger value does not overshadow the effects of the feature with a smaller value when learning a classifier . This becomes particularly important if the feature with smaller values actually contributes to ... | How and why do normalization and feature scaling work?
I think that this is done simply so that the feature with a larger value does not overshadow the effects of the feature with a smaller value when learning a classifier . This becomes particularly impo |
2,734 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | For $x_2$ and $x_1$ close to each other, the percent change $\frac{x_2-x_1}{x_1}$ approximates the log difference $\log x_2 - \log x_1$.
Why does the percent change approximate the log difference?
An idea from calculus is that you can approximate a smooth function with a line. The linear approximation is simply the fir... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | For $x_2$ and $x_1$ close to each other, the percent change $\frac{x_2-x_1}{x_1}$ approximates the log difference $\log x_2 - \log x_1$.
Why does the percent change approximate the log difference?
An | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
For $x_2$ and $x_1$ close to each other, the percent change $\frac{x_2-x_1}{x_1}$ approximates the log difference $\log x_2 - \log x_1$.
Why does the percent change approximate the log difference?
An idea from calculus is ... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
For $x_2$ and $x_1$ close to each other, the percent change $\frac{x_2-x_1}{x_1}$ approximates the log difference $\log x_2 - \log x_1$.
Why does the percent change approximate the log difference?
An |
2,735 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | Here's a version for dummies...
We have the model $Y= \beta_o+\beta_1X+\varepsilon$ - a simple straight line through the data cloud - and we know that once we estimate the coefficients, a $1\text{-unit}$ increase in the prior value of $X=x_1$ will result in a increase of $\hat \beta_1$ in the value of $Y$, from $Y=y_1$... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | Here's a version for dummies...
We have the model $Y= \beta_o+\beta_1X+\varepsilon$ - a simple straight line through the data cloud - and we know that once we estimate the coefficients, a $1\text{-uni | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
Here's a version for dummies...
We have the model $Y= \beta_o+\beta_1X+\varepsilon$ - a simple straight line through the data cloud - and we know that once we estimate the coefficients, a $1\text{-unit}$ increase in the pr... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
Here's a version for dummies...
We have the model $Y= \beta_o+\beta_1X+\varepsilon$ - a simple straight line through the data cloud - and we know that once we estimate the coefficients, a $1\text{-uni |
2,736 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | Say you have a model $$\ln y = A+B x$$
Take a derivative of a log:
$$\frac{d}{dx}\ln y\equiv\frac{1}{y}\frac{dy}{dx}=B$$
Now you can see that the slope $b$ is now a slope of the relative change of $y$:
$$\frac{dy}{y}=B dx$$
If you didn't have the log transform then you'd get a slope of absolute change of $y$:
$$dy=B d... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | Say you have a model $$\ln y = A+B x$$
Take a derivative of a log:
$$\frac{d}{dx}\ln y\equiv\frac{1}{y}\frac{dy}{dx}=B$$
Now you can see that the slope $b$ is now a slope of the relative change of $y | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
Say you have a model $$\ln y = A+B x$$
Take a derivative of a log:
$$\frac{d}{dx}\ln y\equiv\frac{1}{y}\frac{dy}{dx}=B$$
Now you can see that the slope $b$ is now a slope of the relative change of $y$:
$$\frac{dy}{y}=B dx... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
Say you have a model $$\ln y = A+B x$$
Take a derivative of a log:
$$\frac{d}{dx}\ln y\equiv\frac{1}{y}\frac{dy}{dx}=B$$
Now you can see that the slope $b$ is now a slope of the relative change of $y |
2,737 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | There are many great explanations in the present answers, but here is another one framed in terms of financial analysis of the accrual of interest on an initial investment. Suppose you have an initial amount of one unit that accrues interest at (nominal) rate $r$ per annum, with interest "compounded" over $n$ periods ... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | There are many great explanations in the present answers, but here is another one framed in terms of financial analysis of the accrual of interest on an initial investment. Suppose you have an initia | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
There are many great explanations in the present answers, but here is another one framed in terms of financial analysis of the accrual of interest on an initial investment. Suppose you have an initial amount of one unit t... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
There are many great explanations in the present answers, but here is another one framed in terms of financial analysis of the accrual of interest on an initial investment. Suppose you have an initia |
2,738 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | These posts all focus on the difference between two values as a proportion of the the first: $\frac{y-x}{x}$ or $\frac{y}{x} - 1$. They explain why
$$\frac{y}{x} - 1 \approx \ln(\frac{y}{x}) = \ln(y) - \ln(x).$$
You might be interested in the difference as a proportion of the average rather than as a proportion of the... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | These posts all focus on the difference between two values as a proportion of the the first: $\frac{y-x}{x}$ or $\frac{y}{x} - 1$. They explain why
$$\frac{y}{x} - 1 \approx \ln(\frac{y}{x}) = \ln(y) | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
These posts all focus on the difference between two values as a proportion of the the first: $\frac{y-x}{x}$ or $\frac{y}{x} - 1$. They explain why
$$\frac{y}{x} - 1 \approx \ln(\frac{y}{x}) = \ln(y) - \ln(x).$$
You might... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
These posts all focus on the difference between two values as a proportion of the the first: $\frac{y-x}{x}$ or $\frac{y}{x} - 1$. They explain why
$$\frac{y}{x} - 1 \approx \ln(\frac{y}{x}) = \ln(y) |
2,739 | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | This answer does not assume a linear regression framework, nor does it rely on any approximations.
First, let's define some terms:
$$
Old=the\ original\ value\ (or\ variable)
$$
$$
New=the\ new\ value\ (or\ variable)
$$
$$
PC = Proportion\ Change
$$
PC also equals PercentChange/100, and has the domain of [-1,inf]. I fi... | Why is it that natural log changes are percentage changes? What is about logs that makes this so? | This answer does not assume a linear regression framework, nor does it rely on any approximations.
First, let's define some terms:
$$
Old=the\ original\ value\ (or\ variable)
$$
$$
New=the\ new\ value | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
This answer does not assume a linear regression framework, nor does it rely on any approximations.
First, let's define some terms:
$$
Old=the\ original\ value\ (or\ variable)
$$
$$
New=the\ new\ value\ (or\ variable)
$$
$$... | Why is it that natural log changes are percentage changes? What is about logs that makes this so?
This answer does not assume a linear regression framework, nor does it rely on any approximations.
First, let's define some terms:
$$
Old=the\ original\ value\ (or\ variable)
$$
$$
New=the\ new\ value |
2,740 | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | Both, categorical cross entropy and sparse categorical cross entropy have the same loss function which you have mentioned above.
The only difference is the format in which you mention $Y_i$ (i,e true labels).
If your $Y_i$'s are one-hot encoded, use categorical_crossentropy.
Examples (for a 3-class classification): [1... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | Both, categorical cross entropy and sparse categorical cross entropy have the same loss function which you have mentioned above.
The only difference is the format in which you mention $Y_i$ (i,e true | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
Both, categorical cross entropy and sparse categorical cross entropy have the same loss function which you have mentioned above.
The only difference is the format in which you mention $Y_i$ (i,e true labels).
If your $Y_i$'s are one-hot encoded, use... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
Both, categorical cross entropy and sparse categorical cross entropy have the same loss function which you have mentioned above.
The only difference is the format in which you mention $Y_i$ (i,e true |
2,741 | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | The formula which you posted in your question refers to binary_crossentropy, not categorical_crossentropy. The former is used when you have only one class. The latter refers to a situation when you have multiple classes and its formula looks like below:
$$J(\textbf{w}) = -\sum_{i=1}^{N} y_i \text{log}(\hat{y}_i).$$
Thi... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | The formula which you posted in your question refers to binary_crossentropy, not categorical_crossentropy. The former is used when you have only one class. The latter refers to a situation when you ha | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
The formula which you posted in your question refers to binary_crossentropy, not categorical_crossentropy. The former is used when you have only one class. The latter refers to a situation when you have multiple classes and its formula looks like be... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
The formula which you posted in your question refers to binary_crossentropy, not categorical_crossentropy. The former is used when you have only one class. The latter refers to a situation when you ha |
2,742 | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | I have no better answer than the links and me too encountered the same question. I just want to point out, that the formula for loss function (cross entropy) seems to be a little bit erroneous (and might be misleading.) One should probably drop the 2nd term in the bracket to have simply $$J(\textbf{w}) = -\frac{1}{N} \... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | I have no better answer than the links and me too encountered the same question. I just want to point out, that the formula for loss function (cross entropy) seems to be a little bit erroneous (and mi | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
I have no better answer than the links and me too encountered the same question. I just want to point out, that the formula for loss function (cross entropy) seems to be a little bit erroneous (and might be misleading.) One should probably drop the ... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
I have no better answer than the links and me too encountered the same question. I just want to point out, that the formula for loss function (cross entropy) seems to be a little bit erroneous (and mi |
2,743 | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | By the nature of your question, it sounds like you have 3 or more categories. However, for the sake of completion I would like to add that if you are dealing with a binary classification, using binary cross entropy might be more appropriate.
Furthermore, be careful to choose the loss and metric properly, since this ca... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other | By the nature of your question, it sounds like you have 3 or more categories. However, for the sake of completion I would like to add that if you are dealing with a binary classification, using binary | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
By the nature of your question, it sounds like you have 3 or more categories. However, for the sake of completion I would like to add that if you are dealing with a binary classification, using binary cross entropy might be more appropriate.
Furthe... | Cross Entropy vs. Sparse Cross Entropy: When to use one over the other
By the nature of your question, it sounds like you have 3 or more categories. However, for the sake of completion I would like to add that if you are dealing with a binary classification, using binary |
2,744 | Multivariate multiple regression in R | Briefly stated, this is because base-R's manova(lm()) uses sequential model comparisons for so-called Type I sum of squares, whereas car's Manova() by default uses model comparisons for Type II sum of squares.
I assume you're familiar with the model-comparison approach to ANOVA or regression analysis. This approach def... | Multivariate multiple regression in R | Briefly stated, this is because base-R's manova(lm()) uses sequential model comparisons for so-called Type I sum of squares, whereas car's Manova() by default uses model comparisons for Type II sum of | Multivariate multiple regression in R
Briefly stated, this is because base-R's manova(lm()) uses sequential model comparisons for so-called Type I sum of squares, whereas car's Manova() by default uses model comparisons for Type II sum of squares.
I assume you're familiar with the model-comparison approach to ANOVA or ... | Multivariate multiple regression in R
Briefly stated, this is because base-R's manova(lm()) uses sequential model comparisons for so-called Type I sum of squares, whereas car's Manova() by default uses model comparisons for Type II sum of |
2,745 | Multivariate multiple regression in R | Well, I still don't have enough points to comment on previous answer and thats why I am writing it as a separate answer, so please pardon me. (If possible please push me over the 50 rep points ;)
So here are the 2cents:
Type I , II and III errors testing are essentially variations due to data being unbalanced. (Defn Un... | Multivariate multiple regression in R | Well, I still don't have enough points to comment on previous answer and thats why I am writing it as a separate answer, so please pardon me. (If possible please push me over the 50 rep points ;)
So h | Multivariate multiple regression in R
Well, I still don't have enough points to comment on previous answer and thats why I am writing it as a separate answer, so please pardon me. (If possible please push me over the 50 rep points ;)
So here are the 2cents:
Type I , II and III errors testing are essentially variations ... | Multivariate multiple regression in R
Well, I still don't have enough points to comment on previous answer and thats why I am writing it as a separate answer, so please pardon me. (If possible please push me over the 50 rep points ;)
So h |
2,746 | One-hot vs dummy encoding in Scikit-learn | Scikit-learn's linear regression model allows users to disable intercept. So for one-hot encoding, should I always set fit_intercept=False? For dummy encoding, fit_intercept should always be set to True? I do not see any "warning" on the website.
For an unregularized linear model with one-hot encoding, yes, you need t... | One-hot vs dummy encoding in Scikit-learn | Scikit-learn's linear regression model allows users to disable intercept. So for one-hot encoding, should I always set fit_intercept=False? For dummy encoding, fit_intercept should always be set to Tr | One-hot vs dummy encoding in Scikit-learn
Scikit-learn's linear regression model allows users to disable intercept. So for one-hot encoding, should I always set fit_intercept=False? For dummy encoding, fit_intercept should always be set to True? I do not see any "warning" on the website.
For an unregularized linear mo... | One-hot vs dummy encoding in Scikit-learn
Scikit-learn's linear regression model allows users to disable intercept. So for one-hot encoding, should I always set fit_intercept=False? For dummy encoding, fit_intercept should always be set to Tr |
2,747 | One-hot vs dummy encoding in Scikit-learn | To add a little to @MatthewDrury's answer regarding this question:
Say, I have 3 categorical variables, each of which has 4 levels. In dummy encoding, 3*4-3=9 variables are built with one intercept. In one-hot encoding, 3*4=12 variables are built without an intercept. Am I correct?
We can examine what the design matr... | One-hot vs dummy encoding in Scikit-learn | To add a little to @MatthewDrury's answer regarding this question:
Say, I have 3 categorical variables, each of which has 4 levels. In dummy encoding, 3*4-3=9 variables are built with one intercept. | One-hot vs dummy encoding in Scikit-learn
To add a little to @MatthewDrury's answer regarding this question:
Say, I have 3 categorical variables, each of which has 4 levels. In dummy encoding, 3*4-3=9 variables are built with one intercept. In one-hot encoding, 3*4=12 variables are built without an intercept. Am I cor... | One-hot vs dummy encoding in Scikit-learn
To add a little to @MatthewDrury's answer regarding this question:
Say, I have 3 categorical variables, each of which has 4 levels. In dummy encoding, 3*4-3=9 variables are built with one intercept. |
2,748 | One-hot vs dummy encoding in Scikit-learn | I totally agree with @Matthew Drury and @Cameron Bieganek's analysis of perfect collinearity and degree of freedom.
However, I want to argue here that we do not need to avoid perfect collinearity if we are using methods such as gradient descends as our optimizer. (Update, I just realized that there are more situations ... | One-hot vs dummy encoding in Scikit-learn | I totally agree with @Matthew Drury and @Cameron Bieganek's analysis of perfect collinearity and degree of freedom.
However, I want to argue here that we do not need to avoid perfect collinearity if w | One-hot vs dummy encoding in Scikit-learn
I totally agree with @Matthew Drury and @Cameron Bieganek's analysis of perfect collinearity and degree of freedom.
However, I want to argue here that we do not need to avoid perfect collinearity if we are using methods such as gradient descends as our optimizer. (Update, I jus... | One-hot vs dummy encoding in Scikit-learn
I totally agree with @Matthew Drury and @Cameron Bieganek's analysis of perfect collinearity and degree of freedom.
However, I want to argue here that we do not need to avoid perfect collinearity if w |
2,749 | How is the minimum of a set of IID random variables distributed? | If the cdf of $X_i$ is denoted by $F(x)$, then the cdf of the minimum is given by $1-[1-F(x)]^n$. | How is the minimum of a set of IID random variables distributed? | If the cdf of $X_i$ is denoted by $F(x)$, then the cdf of the minimum is given by $1-[1-F(x)]^n$. | How is the minimum of a set of IID random variables distributed?
If the cdf of $X_i$ is denoted by $F(x)$, then the cdf of the minimum is given by $1-[1-F(x)]^n$. | How is the minimum of a set of IID random variables distributed?
If the cdf of $X_i$ is denoted by $F(x)$, then the cdf of the minimum is given by $1-[1-F(x)]^n$. |
2,750 | How is the minimum of a set of IID random variables distributed? | If the CDF of $X_i$ is denoted by $F(x)$, then the CDF of the minimum is given by $1-[1-F(x)]^n$.
Reasoning: given $n$ random variables, the probability $P(Y\leq y) = P(\min(X_1\dots X_n)\leq y)$ implies that at least one $X_i$ is smaller than $y$.
The probability that at least one $X_i$ is smaller than $y$ is equivale... | How is the minimum of a set of IID random variables distributed? | If the CDF of $X_i$ is denoted by $F(x)$, then the CDF of the minimum is given by $1-[1-F(x)]^n$.
Reasoning: given $n$ random variables, the probability $P(Y\leq y) = P(\min(X_1\dots X_n)\leq y)$ impl | How is the minimum of a set of IID random variables distributed?
If the CDF of $X_i$ is denoted by $F(x)$, then the CDF of the minimum is given by $1-[1-F(x)]^n$.
Reasoning: given $n$ random variables, the probability $P(Y\leq y) = P(\min(X_1\dots X_n)\leq y)$ implies that at least one $X_i$ is smaller than $y$.
The pr... | How is the minimum of a set of IID random variables distributed?
If the CDF of $X_i$ is denoted by $F(x)$, then the CDF of the minimum is given by $1-[1-F(x)]^n$.
Reasoning: given $n$ random variables, the probability $P(Y\leq y) = P(\min(X_1\dots X_n)\leq y)$ impl |
2,751 | How is the minimum of a set of IID random variables distributed? | Rob Hyndman gave the easy exact answer for a fixed n. If you're interested in asymptotic behavior for large n, this is handled in the field of extreme value theory. There is a small family of possible limiting distributions; see for example the first chapters of this book. | How is the minimum of a set of IID random variables distributed? | Rob Hyndman gave the easy exact answer for a fixed n. If you're interested in asymptotic behavior for large n, this is handled in the field of extreme value theory. There is a small family of possib | How is the minimum of a set of IID random variables distributed?
Rob Hyndman gave the easy exact answer for a fixed n. If you're interested in asymptotic behavior for large n, this is handled in the field of extreme value theory. There is a small family of possible limiting distributions; see for example the first ch... | How is the minimum of a set of IID random variables distributed?
Rob Hyndman gave the easy exact answer for a fixed n. If you're interested in asymptotic behavior for large n, this is handled in the field of extreme value theory. There is a small family of possib |
2,752 | Optimization when Cost Function Slow to Evaluate | TL;DR
I recommend using LIPO. It is provably correct and provably better than pure random search (PRS). It is also extremely simple to implement, and has no hyperparameters. I have not conducted an analysis that compares LIPO to BO, but my expectation is that the simplicity and efficiency of LIPO imply that it will out... | Optimization when Cost Function Slow to Evaluate | TL;DR
I recommend using LIPO. It is provably correct and provably better than pure random search (PRS). It is also extremely simple to implement, and has no hyperparameters. I have not conducted an an | Optimization when Cost Function Slow to Evaluate
TL;DR
I recommend using LIPO. It is provably correct and provably better than pure random search (PRS). It is also extremely simple to implement, and has no hyperparameters. I have not conducted an analysis that compares LIPO to BO, but my expectation is that the simplic... | Optimization when Cost Function Slow to Evaluate
TL;DR
I recommend using LIPO. It is provably correct and provably better than pure random search (PRS). It is also extremely simple to implement, and has no hyperparameters. I have not conducted an an |
2,753 | Optimization when Cost Function Slow to Evaluate | The literature on evaluation of expensive black-box function is quite vast and it is usually based on surrogate-model methods, as other people pointed out. Black-box here means that little is known about the underlying function, the only thing you can do is evaluate $f(x)$ at a chosen point $x$ (gradients are usually n... | Optimization when Cost Function Slow to Evaluate | The literature on evaluation of expensive black-box function is quite vast and it is usually based on surrogate-model methods, as other people pointed out. Black-box here means that little is known ab | Optimization when Cost Function Slow to Evaluate
The literature on evaluation of expensive black-box function is quite vast and it is usually based on surrogate-model methods, as other people pointed out. Black-box here means that little is known about the underlying function, the only thing you can do is evaluate $f(x... | Optimization when Cost Function Slow to Evaluate
The literature on evaluation of expensive black-box function is quite vast and it is usually based on surrogate-model methods, as other people pointed out. Black-box here means that little is known ab |
2,754 | Optimization when Cost Function Slow to Evaluate | I don't know the algorithms myself, but I believe the kind of optimization algorithm that you are looking for is derivative-free optimization, which is used when the objective is costly or noisy.
For example, take a look at this paper (Björkman, M. & Holmström, K. "Global Optimization of Costly Nonconvex Functions Us... | Optimization when Cost Function Slow to Evaluate | I don't know the algorithms myself, but I believe the kind of optimization algorithm that you are looking for is derivative-free optimization, which is used when the objective is costly or noisy.
Fo | Optimization when Cost Function Slow to Evaluate
I don't know the algorithms myself, but I believe the kind of optimization algorithm that you are looking for is derivative-free optimization, which is used when the objective is costly or noisy.
For example, take a look at this paper (Björkman, M. & Holmström, K. "Glo... | Optimization when Cost Function Slow to Evaluate
I don't know the algorithms myself, but I believe the kind of optimization algorithm that you are looking for is derivative-free optimization, which is used when the objective is costly or noisy.
Fo |
2,755 | Optimization when Cost Function Slow to Evaluate | You are not alone.
Expensive-to-evaluate systems are very common in engineering, such as finite element method (FEM) models and computational fluid dynamics (CFD) models. Optimization of these computatationaly expensive models is very needed and challenge because evoluationary algorithms often needs tens of thouands of... | Optimization when Cost Function Slow to Evaluate | You are not alone.
Expensive-to-evaluate systems are very common in engineering, such as finite element method (FEM) models and computational fluid dynamics (CFD) models. Optimization of these computa | Optimization when Cost Function Slow to Evaluate
You are not alone.
Expensive-to-evaluate systems are very common in engineering, such as finite element method (FEM) models and computational fluid dynamics (CFD) models. Optimization of these computatationaly expensive models is very needed and challenge because evoluat... | Optimization when Cost Function Slow to Evaluate
You are not alone.
Expensive-to-evaluate systems are very common in engineering, such as finite element method (FEM) models and computational fluid dynamics (CFD) models. Optimization of these computa |
2,756 | Optimization when Cost Function Slow to Evaluate | The two simple strategies that I have successfully used in the past are:
If possible, try to find a simpler surrogate function approximating your full cost function evaluation -- typical an analytical model replacing a simulation. Optimize this simpler function. Then validate and fine tune the resulting solution wit... | Optimization when Cost Function Slow to Evaluate | The two simple strategies that I have successfully used in the past are:
If possible, try to find a simpler surrogate function approximating your full cost function evaluation -- typical an analytica | Optimization when Cost Function Slow to Evaluate
The two simple strategies that I have successfully used in the past are:
If possible, try to find a simpler surrogate function approximating your full cost function evaluation -- typical an analytical model replacing a simulation. Optimize this simpler function. Then ... | Optimization when Cost Function Slow to Evaluate
The two simple strategies that I have successfully used in the past are:
If possible, try to find a simpler surrogate function approximating your full cost function evaluation -- typical an analytica |
2,757 | Optimization when Cost Function Slow to Evaluate | There are many tricks used in stochastic gradient descent that can be also applied to objective function evaluation. The overall idea is trying to approximate the objective function using a subset of data.
My answers in these two posts discuss why stochastic gradient descent works: the intuition behind it is to approxi... | Optimization when Cost Function Slow to Evaluate | There are many tricks used in stochastic gradient descent that can be also applied to objective function evaluation. The overall idea is trying to approximate the objective function using a subset of | Optimization when Cost Function Slow to Evaluate
There are many tricks used in stochastic gradient descent that can be also applied to objective function evaluation. The overall idea is trying to approximate the objective function using a subset of data.
My answers in these two posts discuss why stochastic gradient des... | Optimization when Cost Function Slow to Evaluate
There are many tricks used in stochastic gradient descent that can be also applied to objective function evaluation. The overall idea is trying to approximate the objective function using a subset of |
2,758 | What method can be used to detect seasonality in data? | A really good way to find periodicity in any regular series of data is to inspect its power spectrum after removing any overall trend. (This lends itself well to automated screening when the total power is normalized to a standard value, such as unity.) The preliminary trend removal (and optional differencing to remo... | What method can be used to detect seasonality in data? | A really good way to find periodicity in any regular series of data is to inspect its power spectrum after removing any overall trend. (This lends itself well to automated screening when the total po | What method can be used to detect seasonality in data?
A really good way to find periodicity in any regular series of data is to inspect its power spectrum after removing any overall trend. (This lends itself well to automated screening when the total power is normalized to a standard value, such as unity.) The preli... | What method can be used to detect seasonality in data?
A really good way to find periodicity in any regular series of data is to inspect its power spectrum after removing any overall trend. (This lends itself well to automated screening when the total po |
2,759 | What method can be used to detect seasonality in data? | Here's an example using monthly data on log unemployment claims from a city in New Jersey (from Stata, only because that's what I analyzed these data in originally).
The heights of the lines indicate the correlation between a variable and the sth lag of itself; the gray area gives you a sense of whether this correlati... | What method can be used to detect seasonality in data? | Here's an example using monthly data on log unemployment claims from a city in New Jersey (from Stata, only because that's what I analyzed these data in originally).
The heights of the lines indicate | What method can be used to detect seasonality in data?
Here's an example using monthly data on log unemployment claims from a city in New Jersey (from Stata, only because that's what I analyzed these data in originally).
The heights of the lines indicate the correlation between a variable and the sth lag of itself; th... | What method can be used to detect seasonality in data?
Here's an example using monthly data on log unemployment claims from a city in New Jersey (from Stata, only because that's what I analyzed these data in originally).
The heights of the lines indicate |
2,760 | What method can be used to detect seasonality in data? | Seasonality can and does often change over time thus summary measures can be quite inadequate to detect structure. One needs to test for transience in ARIMA coefficients and often changes in the “seasonal dummies”. For example in a 10 year horizon there may not have been a June effect for the first k years but the last... | What method can be used to detect seasonality in data? | Seasonality can and does often change over time thus summary measures can be quite inadequate to detect structure. One needs to test for transience in ARIMA coefficients and often changes in the “seas | What method can be used to detect seasonality in data?
Seasonality can and does often change over time thus summary measures can be quite inadequate to detect structure. One needs to test for transience in ARIMA coefficients and often changes in the “seasonal dummies”. For example in a 10 year horizon there may not hav... | What method can be used to detect seasonality in data?
Seasonality can and does often change over time thus summary measures can be quite inadequate to detect structure. One needs to test for transience in ARIMA coefficients and often changes in the “seas |
2,761 | What method can be used to detect seasonality in data? | Continuous wavelet transform can show the seasonality as well. Because the assumption of periodogram is the seasonality is stationary, wavelet is better than periodogram since it allows the change of seasonality along the time. Just like periodogram decomposes the time series into sine or cosine waves of different freq... | What method can be used to detect seasonality in data? | Continuous wavelet transform can show the seasonality as well. Because the assumption of periodogram is the seasonality is stationary, wavelet is better than periodogram since it allows the change of | What method can be used to detect seasonality in data?
Continuous wavelet transform can show the seasonality as well. Because the assumption of periodogram is the seasonality is stationary, wavelet is better than periodogram since it allows the change of seasonality along the time. Just like periodogram decomposes the ... | What method can be used to detect seasonality in data?
Continuous wavelet transform can show the seasonality as well. Because the assumption of periodogram is the seasonality is stationary, wavelet is better than periodogram since it allows the change of |
2,762 | What method can be used to detect seasonality in data? | Charlie's answer is good, and it's where I'd start. If you don't want to use ACF graphs, you could create k-1 dummy variables for the k time periods present. Then you can see if the dummy variables are significant in a regression with the dummy variables (and likely a trend term).
If your data is quarterly:
dummy Q2 i... | What method can be used to detect seasonality in data? | Charlie's answer is good, and it's where I'd start. If you don't want to use ACF graphs, you could create k-1 dummy variables for the k time periods present. Then you can see if the dummy variables a | What method can be used to detect seasonality in data?
Charlie's answer is good, and it's where I'd start. If you don't want to use ACF graphs, you could create k-1 dummy variables for the k time periods present. Then you can see if the dummy variables are significant in a regression with the dummy variables (and like... | What method can be used to detect seasonality in data?
Charlie's answer is good, and it's where I'd start. If you don't want to use ACF graphs, you could create k-1 dummy variables for the k time periods present. Then you can see if the dummy variables a |
2,763 | What method can be used to detect seasonality in data? | I"m a bit new to R myself, but my understanding of the ACF function is that if the vertical line goes above the top dashed line or below the bottom dashed line, there is some autoregression (including seasonality). Try creating a vector of sine | What method can be used to detect seasonality in data? | I"m a bit new to R myself, but my understanding of the ACF function is that if the vertical line goes above the top dashed line or below the bottom dashed line, there is some autoregression (including | What method can be used to detect seasonality in data?
I"m a bit new to R myself, but my understanding of the ACF function is that if the vertical line goes above the top dashed line or below the bottom dashed line, there is some autoregression (including seasonality). Try creating a vector of sine | What method can be used to detect seasonality in data?
I"m a bit new to R myself, but my understanding of the ACF function is that if the vertical line goes above the top dashed line or below the bottom dashed line, there is some autoregression (including |
2,764 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | The Pearson and Spearman correlation are defined as long as you have some $0$s and some $1$s for both of two binary variables, say $y$ and $x$. It is easy to get a good qualitative idea of what they mean by thinking of a scatter plot of the two variables. Clearly, there are only four possibilities $(0,0), (0,1), (1, 0)... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | The Pearson and Spearman correlation are defined as long as you have some $0$s and some $1$s for both of two binary variables, say $y$ and $x$. It is easy to get a good qualitative idea of what they m | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
The Pearson and Spearman correlation are defined as long as you have some $0$s and some $1$s for both of two binary variables, say $y$ and $x$. It is easy to get a good qualitative idea of what they mean by thinking of a scatter ... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
The Pearson and Spearman correlation are defined as long as you have some $0$s and some $1$s for both of two binary variables, say $y$ and $x$. It is easy to get a good qualitative idea of what they m |
2,765 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | There are specialised similarity metrics for binary vectors, such as:
Jaccard-Needham
Dice
Yule
Russell-Rao
Sokal-Michener
Rogers-Tanimoto
Kulzinsky
etc.
For details, see here. | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | There are specialised similarity metrics for binary vectors, such as:
Jaccard-Needham
Dice
Yule
Russell-Rao
Sokal-Michener
Rogers-Tanimoto
Kulzinsky
etc.
For details, see here. | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
There are specialised similarity metrics for binary vectors, such as:
Jaccard-Needham
Dice
Yule
Russell-Rao
Sokal-Michener
Rogers-Tanimoto
Kulzinsky
etc.
For details, see here. | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
There are specialised similarity metrics for binary vectors, such as:
Jaccard-Needham
Dice
Yule
Russell-Rao
Sokal-Michener
Rogers-Tanimoto
Kulzinsky
etc.
For details, see here. |
2,766 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | I would not advise to use Pearson's correlation coefficient for binary data, see the following counter-example:
set.seed(10)
a = rbinom(n=100, size=1, prob=0.9)
b = rbinom(n=100, size=1, prob=0.9)
in most cases both give a 1
table(a,b)
> table(a,b)
b
a 0 1
0 0 3
1 9 88
but the correlation does not s... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | I would not advise to use Pearson's correlation coefficient for binary data, see the following counter-example:
set.seed(10)
a = rbinom(n=100, size=1, prob=0.9)
b = rbinom(n=100, size=1, prob=0.9)
| Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
I would not advise to use Pearson's correlation coefficient for binary data, see the following counter-example:
set.seed(10)
a = rbinom(n=100, size=1, prob=0.9)
b = rbinom(n=100, size=1, prob=0.9)
in most cases both give a 1
... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
I would not advise to use Pearson's correlation coefficient for binary data, see the following counter-example:
set.seed(10)
a = rbinom(n=100, size=1, prob=0.9)
b = rbinom(n=100, size=1, prob=0.9)
|
2,767 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | Arne's response above isn't quite right. Correlation is a measure of dependence between variables. The samples A and B are both independent draws, although they are from the same distribution, so we should expect ~0 correlation.
Running a similar simulation and creating a new variable c that is dependent on the value o... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | Arne's response above isn't quite right. Correlation is a measure of dependence between variables. The samples A and B are both independent draws, although they are from the same distribution, so we s | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
Arne's response above isn't quite right. Correlation is a measure of dependence between variables. The samples A and B are both independent draws, although they are from the same distribution, so we should expect ~0 correlation.
... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
Arne's response above isn't quite right. Correlation is a measure of dependence between variables. The samples A and B are both independent draws, although they are from the same distribution, so we s |
2,768 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | A possible issue with using the Pearson correlation for two dichotomous variables is that the correlation may be sensitive to the "levels" of the variables, i.e. the rates at which the variables are 1. Specifically, suppose that you think the two dichotomous variables (X,Y) are generated by underlying latent continuous... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors? | A possible issue with using the Pearson correlation for two dichotomous variables is that the correlation may be sensitive to the "levels" of the variables, i.e. the rates at which the variables are 1 | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
A possible issue with using the Pearson correlation for two dichotomous variables is that the correlation may be sensitive to the "levels" of the variables, i.e. the rates at which the variables are 1. Specifically, suppose that ... | Is it meaningful to calculate Pearson or Spearman correlation between two Boolean vectors?
A possible issue with using the Pearson correlation for two dichotomous variables is that the correlation may be sensitive to the "levels" of the variables, i.e. the rates at which the variables are 1 |
2,769 | What are good initial weights in a neural network? | I assume you are using logistic neurons, and that you are training by gradient descent/back-propagation.
The logistic function is close to flat for large positive or negative inputs. The derivative at an input of $2$ is about $1/10$, but at $10$ the derivative is about $1/22000$ . This means that if the input of a log... | What are good initial weights in a neural network? | I assume you are using logistic neurons, and that you are training by gradient descent/back-propagation.
The logistic function is close to flat for large positive or negative inputs. The derivative a | What are good initial weights in a neural network?
I assume you are using logistic neurons, and that you are training by gradient descent/back-propagation.
The logistic function is close to flat for large positive or negative inputs. The derivative at an input of $2$ is about $1/10$, but at $10$ the derivative is abou... | What are good initial weights in a neural network?
I assume you are using logistic neurons, and that you are training by gradient descent/back-propagation.
The logistic function is close to flat for large positive or negative inputs. The derivative a |
2,770 | What are good initial weights in a neural network? | [1] addresses the question:
First, weights shouldn't be set to zeros in order to break the symmetry when backprogragating:
Biases can generally be initialized to zero but weights need to be initialized carefully to break the symmetry between hidden units of the same layer. Because different output units receive differ... | What are good initial weights in a neural network? | [1] addresses the question:
First, weights shouldn't be set to zeros in order to break the symmetry when backprogragating:
Biases can generally be initialized to zero but weights need to be initializ | What are good initial weights in a neural network?
[1] addresses the question:
First, weights shouldn't be set to zeros in order to break the symmetry when backprogragating:
Biases can generally be initialized to zero but weights need to be initialized carefully to break the symmetry between hidden units of the same l... | What are good initial weights in a neural network?
[1] addresses the question:
First, weights shouldn't be set to zeros in order to break the symmetry when backprogragating:
Biases can generally be initialized to zero but weights need to be initializ |
2,771 | What are good initial weights in a neural network? | The following explanation is taken from the book: Neural Networks for Pattern Recognition by Christopher Bishop. Great book!
Assume you have previously whitened the inputs to the input units, i.e. $$<x_{i}> = 0$$ and $$<x_{i}^{2}> = 1$$
The question is: how to best choose the weights?. The idea is to pick values of the... | What are good initial weights in a neural network? | The following explanation is taken from the book: Neural Networks for Pattern Recognition by Christopher Bishop. Great book!
Assume you have previously whitened the inputs to the input units, i.e. $$< | What are good initial weights in a neural network?
The following explanation is taken from the book: Neural Networks for Pattern Recognition by Christopher Bishop. Great book!
Assume you have previously whitened the inputs to the input units, i.e. $$<x_{i}> = 0$$ and $$<x_{i}^{2}> = 1$$
The question is: how to best cho... | What are good initial weights in a neural network?
The following explanation is taken from the book: Neural Networks for Pattern Recognition by Christopher Bishop. Great book!
Assume you have previously whitened the inputs to the input units, i.e. $$< |
2,772 | What are good initial weights in a neural network? | Well just as an update, Delving Deep into Rectifiers: Surpassing Human-Level Performance n ImageNet Classification by He et al introduced an initialization specifically with initialization w = U([0,n]) * sqrt(2.0/n) where n is the number of inputs of your NN. I have seen this initialization used in many recent works (a... | What are good initial weights in a neural network? | Well just as an update, Delving Deep into Rectifiers: Surpassing Human-Level Performance n ImageNet Classification by He et al introduced an initialization specifically with initialization w = U([0,n] | What are good initial weights in a neural network?
Well just as an update, Delving Deep into Rectifiers: Surpassing Human-Level Performance n ImageNet Classification by He et al introduced an initialization specifically with initialization w = U([0,n]) * sqrt(2.0/n) where n is the number of inputs of your NN. I have se... | What are good initial weights in a neural network?
Well just as an update, Delving Deep into Rectifiers: Surpassing Human-Level Performance n ImageNet Classification by He et al introduced an initialization specifically with initialization w = U([0,n] |
2,773 | What are good initial weights in a neural network? | The idea is that you want to initialize the weights in a way that ensures good forward and backward data flow through the network. That is, you don't want the activations to be consistently shrinking or increasing as you progress through the network.
This image shows the activations of a 5 layer ReLU Multi-Layer Percep... | What are good initial weights in a neural network? | The idea is that you want to initialize the weights in a way that ensures good forward and backward data flow through the network. That is, you don't want the activations to be consistently shrinking | What are good initial weights in a neural network?
The idea is that you want to initialize the weights in a way that ensures good forward and backward data flow through the network. That is, you don't want the activations to be consistently shrinking or increasing as you progress through the network.
This image shows t... | What are good initial weights in a neural network?
The idea is that you want to initialize the weights in a way that ensures good forward and backward data flow through the network. That is, you don't want the activations to be consistently shrinking |
2,774 | What are good initial weights in a neural network? | One other technique that alleviates the problem of weight initialization is Batch Normalization. It acts to standardize the mean and variance of each unit in order to stabilize learning as described in the original paper. In practice, networks that use Batch Normalization (BN) are significantly more robust to bad initi... | What are good initial weights in a neural network? | One other technique that alleviates the problem of weight initialization is Batch Normalization. It acts to standardize the mean and variance of each unit in order to stabilize learning as described i | What are good initial weights in a neural network?
One other technique that alleviates the problem of weight initialization is Batch Normalization. It acts to standardize the mean and variance of each unit in order to stabilize learning as described in the original paper. In practice, networks that use Batch Normalizat... | What are good initial weights in a neural network?
One other technique that alleviates the problem of weight initialization is Batch Normalization. It acts to standardize the mean and variance of each unit in order to stabilize learning as described i |
2,775 | What are good initial weights in a neural network? | There are two distinct ideas in this heuristic:
Initialize the weights to be small - in addition to Douglas Zare excellent answer about sigmoid activations, the problem is more general. Even when the gradients are of "good" magnitude (e.g., using ReLU activations) training is hampered with big weights. Think about 2 n... | What are good initial weights in a neural network? | There are two distinct ideas in this heuristic:
Initialize the weights to be small - in addition to Douglas Zare excellent answer about sigmoid activations, the problem is more general. Even when the | What are good initial weights in a neural network?
There are two distinct ideas in this heuristic:
Initialize the weights to be small - in addition to Douglas Zare excellent answer about sigmoid activations, the problem is more general. Even when the gradients are of "good" magnitude (e.g., using ReLU activations) tra... | What are good initial weights in a neural network?
There are two distinct ideas in this heuristic:
Initialize the weights to be small - in addition to Douglas Zare excellent answer about sigmoid activations, the problem is more general. Even when the |
2,776 | Diagnostics for logistic regression? | A few newer techniques I have come across for assessing the fit of logistic regression models come from political science journals:
Greenhill, Brian, Michael D. Ward & Audrey Sacks. 2011. The separation plot: A new visual method for evaluating the fit of binary models. American Journal of Political Science 55(4):991-1... | Diagnostics for logistic regression? | A few newer techniques I have come across for assessing the fit of logistic regression models come from political science journals:
Greenhill, Brian, Michael D. Ward & Audrey Sacks. 2011. The separat | Diagnostics for logistic regression?
A few newer techniques I have come across for assessing the fit of logistic regression models come from political science journals:
Greenhill, Brian, Michael D. Ward & Audrey Sacks. 2011. The separation plot: A new visual method for evaluating the fit of binary models. American Jou... | Diagnostics for logistic regression?
A few newer techniques I have come across for assessing the fit of logistic regression models come from political science journals:
Greenhill, Brian, Michael D. Ward & Audrey Sacks. 2011. The separat |
2,777 | Diagnostics for logistic regression? | The question was not well enough motivated. There has to be a reason to run model diagnostics, such as
Potential to change the model to make it better
Not knowing which directed tests to use (i.e., tests of non-linearity or interaction)
Failing to grasp that changing the model can easily distort statistical inference... | Diagnostics for logistic regression? | The question was not well enough motivated. There has to be a reason to run model diagnostics, such as
Potential to change the model to make it better
Not knowing which directed tests to use (i.e., | Diagnostics for logistic regression?
The question was not well enough motivated. There has to be a reason to run model diagnostics, such as
Potential to change the model to make it better
Not knowing which directed tests to use (i.e., tests of non-linearity or interaction)
Failing to grasp that changing the model can... | Diagnostics for logistic regression?
The question was not well enough motivated. There has to be a reason to run model diagnostics, such as
Potential to change the model to make it better
Not knowing which directed tests to use (i.e., |
2,778 | Diagnostics for logistic regression? | This thread is quite old, but I thought it would be useful to add that, since recently, you can use the DHARMa R package to transform the residuals of any GL(M)M into a standardized space. Once this is done, you can visually assess / test residual problems such as deviations from the distribution, residual dependency o... | Diagnostics for logistic regression? | This thread is quite old, but I thought it would be useful to add that, since recently, you can use the DHARMa R package to transform the residuals of any GL(M)M into a standardized space. Once this i | Diagnostics for logistic regression?
This thread is quite old, but I thought it would be useful to add that, since recently, you can use the DHARMa R package to transform the residuals of any GL(M)M into a standardized space. Once this is done, you can visually assess / test residual problems such as deviations from th... | Diagnostics for logistic regression?
This thread is quite old, but I thought it would be useful to add that, since recently, you can use the DHARMa R package to transform the residuals of any GL(M)M into a standardized space. Once this i |
2,779 | Free resources for learning R | Some useful R links (find out the link that suits you):
Intro:
for R basics http://cran.r-project.org/doc/contrib/usingR.pdf
for data manipulation http://had.co.nz/plyr/plyr-intro-090510.pdf
http://portal.stats.ox.ac.uk/userdata/ruth/APTS2012/APTS.html
Interactive intro to R programming language https://www.datacamp.c... | Free resources for learning R | Some useful R links (find out the link that suits you):
Intro:
for R basics http://cran.r-project.org/doc/contrib/usingR.pdf
for data manipulation http://had.co.nz/plyr/plyr-intro-090510.pdf
http://p | Free resources for learning R
Some useful R links (find out the link that suits you):
Intro:
for R basics http://cran.r-project.org/doc/contrib/usingR.pdf
for data manipulation http://had.co.nz/plyr/plyr-intro-090510.pdf
http://portal.stats.ox.ac.uk/userdata/ruth/APTS2012/APTS.html
Interactive intro to R programming l... | Free resources for learning R
Some useful R links (find out the link that suits you):
Intro:
for R basics http://cran.r-project.org/doc/contrib/usingR.pdf
for data manipulation http://had.co.nz/plyr/plyr-intro-090510.pdf
http://p |
2,780 | Free resources for learning R | If I had to choose one thing, make sure that you read "The R Inferno".
There are many good resources on the R homepage, but in particular, read "An Introduction to R" and "The R Language Definition". | Free resources for learning R | If I had to choose one thing, make sure that you read "The R Inferno".
There are many good resources on the R homepage, but in particular, read "An Introduction to R" and "The R Language Definition". | Free resources for learning R
If I had to choose one thing, make sure that you read "The R Inferno".
There are many good resources on the R homepage, but in particular, read "An Introduction to R" and "The R Language Definition". | Free resources for learning R
If I had to choose one thing, make sure that you read "The R Inferno".
There are many good resources on the R homepage, but in particular, read "An Introduction to R" and "The R Language Definition". |
2,781 | Free resources for learning R | Quick-R can be a good place to start.
A little bit data mining oriented R and Data Mining resources: Examples and Case Studies and R Reference Card for Data Mining. | Free resources for learning R | Quick-R can be a good place to start.
A little bit data mining oriented R and Data Mining resources: Examples and Case Studies and R Reference Card for Data Mining. | Free resources for learning R
Quick-R can be a good place to start.
A little bit data mining oriented R and Data Mining resources: Examples and Case Studies and R Reference Card for Data Mining. | Free resources for learning R
Quick-R can be a good place to start.
A little bit data mining oriented R and Data Mining resources: Examples and Case Studies and R Reference Card for Data Mining. |
2,782 | Free resources for learning R | If you like learning through videos, I collated a list of R training videos.
I also prepared a general post on learning R with suggestions on books, online manuals, blogs, videos, user interfaces, and more. | Free resources for learning R | If you like learning through videos, I collated a list of R training videos.
I also prepared a general post on learning R with suggestions on books, online manuals, blogs, videos, user interfaces, an | Free resources for learning R
If you like learning through videos, I collated a list of R training videos.
I also prepared a general post on learning R with suggestions on books, online manuals, blogs, videos, user interfaces, and more. | Free resources for learning R
If you like learning through videos, I collated a list of R training videos.
I also prepared a general post on learning R with suggestions on books, online manuals, blogs, videos, user interfaces, an |
2,783 | Free resources for learning R | Try IPSUR, Introduction to Probability and Statistics Using R. It's a free book, free in the GNU sense of the word.
http://ipsur.r-forge.r-project.org/book/index.php
It's definitely open source - on the download page you can download the LaTeX source or the lyx source used to generate this. | Free resources for learning R | Try IPSUR, Introduction to Probability and Statistics Using R. It's a free book, free in the GNU sense of the word.
http://ipsur.r-forge.r-project.org/book/index.php
It's definitely open source - on | Free resources for learning R
Try IPSUR, Introduction to Probability and Statistics Using R. It's a free book, free in the GNU sense of the word.
http://ipsur.r-forge.r-project.org/book/index.php
It's definitely open source - on the download page you can download the LaTeX source or the lyx source used to generate thi... | Free resources for learning R
Try IPSUR, Introduction to Probability and Statistics Using R. It's a free book, free in the GNU sense of the word.
http://ipsur.r-forge.r-project.org/book/index.php
It's definitely open source - on |
2,784 | Free resources for learning R | The official guides are pretty nice; check out http://cran.r-project.org/manuals.html . There is also a lot of contributed documentation there. | Free resources for learning R | The official guides are pretty nice; check out http://cran.r-project.org/manuals.html . There is also a lot of contributed documentation there. | Free resources for learning R
The official guides are pretty nice; check out http://cran.r-project.org/manuals.html . There is also a lot of contributed documentation there. | Free resources for learning R
The official guides are pretty nice; check out http://cran.r-project.org/manuals.html . There is also a lot of contributed documentation there. |
2,785 | Free resources for learning R | If you're an economist/econometrician then Grant Farnworth's paper on using R is indispensable and is available on CRAN at:
http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf | Free resources for learning R | If you're an economist/econometrician then Grant Farnworth's paper on using R is indispensable and is available on CRAN at:
http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf | Free resources for learning R
If you're an economist/econometrician then Grant Farnworth's paper on using R is indispensable and is available on CRAN at:
http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf | Free resources for learning R
If you're an economist/econometrician then Grant Farnworth's paper on using R is indispensable and is available on CRAN at:
http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf |
2,786 | Free resources for learning R | If you have experience in other languages, these "R Rosetta Stone" videos may be useful:
Python
MATLAB
SQL
These are all included in the video list added by Jeromy, so big +1 for his list. | Free resources for learning R | If you have experience in other languages, these "R Rosetta Stone" videos may be useful:
Python
MATLAB
SQL
These are all included in the video list added by Jeromy, so big +1 for his list. | Free resources for learning R
If you have experience in other languages, these "R Rosetta Stone" videos may be useful:
Python
MATLAB
SQL
These are all included in the video list added by Jeromy, so big +1 for his list. | Free resources for learning R
If you have experience in other languages, these "R Rosetta Stone" videos may be useful:
Python
MATLAB
SQL
These are all included in the video list added by Jeromy, so big +1 for his list. |
2,787 | Free resources for learning R | One resource is 'Some hints for the R beginner' at
http://www.burns-stat.com/pages/Tutor/hints_R_begin.html | Free resources for learning R | One resource is 'Some hints for the R beginner' at
http://www.burns-stat.com/pages/Tutor/hints_R_begin.html | Free resources for learning R
One resource is 'Some hints for the R beginner' at
http://www.burns-stat.com/pages/Tutor/hints_R_begin.html | Free resources for learning R
One resource is 'Some hints for the R beginner' at
http://www.burns-stat.com/pages/Tutor/hints_R_begin.html |
2,788 | Free resources for learning R | I have written a document that is freely available at my website and on CRAN. See the linked page:
icebreakeR
The datasets that are used in the document are also linked from that page. Feedback is welcome and appreciated!
Andrew | Free resources for learning R | I have written a document that is freely available at my website and on CRAN. See the linked page:
icebreakeR
The datasets that are used in the document are also linked from that page. Feedback is we | Free resources for learning R
I have written a document that is freely available at my website and on CRAN. See the linked page:
icebreakeR
The datasets that are used in the document are also linked from that page. Feedback is welcome and appreciated!
Andrew | Free resources for learning R
I have written a document that is freely available at my website and on CRAN. See the linked page:
icebreakeR
The datasets that are used in the document are also linked from that page. Feedback is we |
2,789 | Free resources for learning R | After you learn the basics, I find the following sites very useful:
R-bloggers.
Subscribing to the Stack overflow R tag. | Free resources for learning R | After you learn the basics, I find the following sites very useful:
R-bloggers.
Subscribing to the Stack overflow R tag. | Free resources for learning R
After you learn the basics, I find the following sites very useful:
R-bloggers.
Subscribing to the Stack overflow R tag. | Free resources for learning R
After you learn the basics, I find the following sites very useful:
R-bloggers.
Subscribing to the Stack overflow R tag. |
2,790 | Free resources for learning R | A large number of short videos that cover a lot of useful tasks with R (91 videos as of March 2013): http://www.twotorials.com/
Here's a nice new interactive online tutorial on the basics of R: http://tryr.codeschool.com/ | Free resources for learning R | A large number of short videos that cover a lot of useful tasks with R (91 videos as of March 2013): http://www.twotorials.com/
Here's a nice new interactive online tutorial on the basics of R: http: | Free resources for learning R
A large number of short videos that cover a lot of useful tasks with R (91 videos as of March 2013): http://www.twotorials.com/
Here's a nice new interactive online tutorial on the basics of R: http://tryr.codeschool.com/ | Free resources for learning R
A large number of short videos that cover a lot of useful tasks with R (91 videos as of March 2013): http://www.twotorials.com/
Here's a nice new interactive online tutorial on the basics of R: http: |
2,791 | Free resources for learning R | The R project website has lots of manuals to start, and I suggest you the Nabble R forum and the R-bloggers site as well. | Free resources for learning R | The R project website has lots of manuals to start, and I suggest you the Nabble R forum and the R-bloggers site as well. | Free resources for learning R
The R project website has lots of manuals to start, and I suggest you the Nabble R forum and the R-bloggers site as well. | Free resources for learning R
The R project website has lots of manuals to start, and I suggest you the Nabble R forum and the R-bloggers site as well. |
2,792 | Free resources for learning R | If you already know another programming language, these notes may help point out some of the ways R might surprise you. | Free resources for learning R | If you already know another programming language, these notes may help point out some of the ways R might surprise you. | Free resources for learning R
If you already know another programming language, these notes may help point out some of the ways R might surprise you. | Free resources for learning R
If you already know another programming language, these notes may help point out some of the ways R might surprise you. |
2,793 | Free resources for learning R | I liked these lectures: Statistical Aspects of Data Mining. The lecturer is solving example problems using R. | Free resources for learning R | I liked these lectures: Statistical Aspects of Data Mining. The lecturer is solving example problems using R. | Free resources for learning R
I liked these lectures: Statistical Aspects of Data Mining. The lecturer is solving example problems using R. | Free resources for learning R
I liked these lectures: Statistical Aspects of Data Mining. The lecturer is solving example problems using R. |
2,794 | Free resources for learning R | If you are coming from a SAS or SPSS background, check out:
http://sites.google.com/site/r4statistics/
This is the companion site to the book, R for SAS and SPSS Users by Robert Muenchen and a free version of the book can be found here. | Free resources for learning R | If you are coming from a SAS or SPSS background, check out:
http://sites.google.com/site/r4statistics/
This is the companion site to the book, R for SAS and SPSS Users by Robert Muenchen and a free ve | Free resources for learning R
If you are coming from a SAS or SPSS background, check out:
http://sites.google.com/site/r4statistics/
This is the companion site to the book, R for SAS and SPSS Users by Robert Muenchen and a free version of the book can be found here. | Free resources for learning R
If you are coming from a SAS or SPSS background, check out:
http://sites.google.com/site/r4statistics/
This is the companion site to the book, R for SAS and SPSS Users by Robert Muenchen and a free ve |
2,795 | Free resources for learning R | One more: R bloggers has many posts with tutorials materials:
http://www.r-bloggers.com/?s=tutorial | Free resources for learning R | One more: R bloggers has many posts with tutorials materials:
http://www.r-bloggers.com/?s=tutorial | Free resources for learning R
One more: R bloggers has many posts with tutorials materials:
http://www.r-bloggers.com/?s=tutorial | Free resources for learning R
One more: R bloggers has many posts with tutorials materials:
http://www.r-bloggers.com/?s=tutorial |
2,796 | Free resources for learning R | There are some very good learning materials here: http://scc.stat.ucla.edu/mini-courses/materials-from-past-mini-courses/spring-2009-mini-course-materials/ | Free resources for learning R | There are some very good learning materials here: http://scc.stat.ucla.edu/mini-courses/materials-from-past-mini-courses/spring-2009-mini-course-materials/ | Free resources for learning R
There are some very good learning materials here: http://scc.stat.ucla.edu/mini-courses/materials-from-past-mini-courses/spring-2009-mini-course-materials/ | Free resources for learning R
There are some very good learning materials here: http://scc.stat.ucla.edu/mini-courses/materials-from-past-mini-courses/spring-2009-mini-course-materials/ |
2,797 | Free resources for learning R | Look for R Users Groups in your area. They are growing around the world.
http://blog.revolutionanalytics.com/local-r-groups.html
If you don't have one then help get one started. I'm sure you will be able to find like minded interested folks.
As for helpful links the Dallas R Users Group has a nice list.
http://www.me... | Free resources for learning R | Look for R Users Groups in your area. They are growing around the world.
http://blog.revolutionanalytics.com/local-r-groups.html
If you don't have one then help get one started. I'm sure you will be | Free resources for learning R
Look for R Users Groups in your area. They are growing around the world.
http://blog.revolutionanalytics.com/local-r-groups.html
If you don't have one then help get one started. I'm sure you will be able to find like minded interested folks.
As for helpful links the Dallas R Users Group ... | Free resources for learning R
Look for R Users Groups in your area. They are growing around the world.
http://blog.revolutionanalytics.com/local-r-groups.html
If you don't have one then help get one started. I'm sure you will be |
2,798 | Free resources for learning R | http://www.datamind.org offers interactive R tutorials, currently focused at real beginners | Free resources for learning R | http://www.datamind.org offers interactive R tutorials, currently focused at real beginners | Free resources for learning R
http://www.datamind.org offers interactive R tutorials, currently focused at real beginners | Free resources for learning R
http://www.datamind.org offers interactive R tutorials, currently focused at real beginners |
2,799 | Free resources for learning R | If you'd like a beginners tutorial to R in the context of Econometrics this may be a good starting point as well: http://www.quandl.com/learn/working-with-quandl-and-r | Free resources for learning R | If you'd like a beginners tutorial to R in the context of Econometrics this may be a good starting point as well: http://www.quandl.com/learn/working-with-quandl-and-r | Free resources for learning R
If you'd like a beginners tutorial to R in the context of Econometrics this may be a good starting point as well: http://www.quandl.com/learn/working-with-quandl-and-r | Free resources for learning R
If you'd like a beginners tutorial to R in the context of Econometrics this may be a good starting point as well: http://www.quandl.com/learn/working-with-quandl-and-r |
2,800 | Performance metrics to evaluate unsupervised learning | In some sense I think this question is unanswerable. I say this because how well a particular unsupervised method performs will largely depend on why one is doing unsupervised learning in the first place, i.e., does the method perform well in the context of your end goal? Obviously this isn't completely true, people wo... | Performance metrics to evaluate unsupervised learning | In some sense I think this question is unanswerable. I say this because how well a particular unsupervised method performs will largely depend on why one is doing unsupervised learning in the first pl | Performance metrics to evaluate unsupervised learning
In some sense I think this question is unanswerable. I say this because how well a particular unsupervised method performs will largely depend on why one is doing unsupervised learning in the first place, i.e., does the method perform well in the context of your end... | Performance metrics to evaluate unsupervised learning
In some sense I think this question is unanswerable. I say this because how well a particular unsupervised method performs will largely depend on why one is doing unsupervised learning in the first pl |
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