broccoli
  • Member for 9 years, 5 months
  • Last seen this week
Free statistical textbooks
5 votes

A write up of probability tutorials and related puzzles along with R code for learning. Hope it helps

View answer
Weighting words based on position in text
Accepted answer
4 votes

This is tricky. Often times, the actual position of a word is not that useful in enhancing any kind of recall you want to do on the document. But you could, for instance, create features around them ...

View answer
What math subjects would you suggest to prepare for data mining and machine learning?
3 votes

If you are looking to bulk up on machine learning/data mining I would strongly urge optimization/linear algebra/statistics and probability. Here is a list of books for probability. Hope that helps.

View answer
Medical Insurance Fraud Detection: Text analysis
3 votes

This a tough one without revealing more about the data. I would say your best bet is to create features out of the plain text file based on subjective data. For example some features could be 1) How ...

View answer
How would you deal with categorical data in a naive Bayesian classifier?
3 votes

For an Naive Bayes classifier, categorical values are the easiest to deal with. All you are really after is P(Feature | Class). This should be easy for the days of the week. Compute P(Monday | Class=...

View answer
The Sleeping Beauty Paradox
3 votes

A simple explanation for this would be that there are 3 ways in which sleeping beauty can wake up two of which are from a Tails toss. So the probability has to be 1/3 for a heads every time she wakes ...

View answer
Does this residual plot look bad?
Accepted answer
2 votes

Have you tried plotting this using ggplot2 in R? It has a nice semi transparency feature with the Cairo package which makes guesstimating the mean for such residual plots easy. For example you could ...

View answer
Econometric model for finding optimal number of workers per sq. meter
Accepted answer
1 votes

The solution might be easier than you think. Why not try something like the following (denote as $x$ the ratio of number of workers to square footage) $ log(sales) = \alpha_0 + \alpha_1x + \alpha_2x^{...

View answer
Linear post-treatment of nonlinear regression
1 votes

A lot would depend on the data set itself. From your graph it appears that the RF(or NN) is missing on training some information. In such cases, you must almost definitely do what you are proposing. ...

View answer
Algorithms for prediction of consumption, based on previous data
1 votes

It is possible to handle such situations. For example you could try and use a simple exponential moving average to predict the next sample point, and then use it again to predict the next to next ...

View answer
Probability of picking a biased coin
1 votes

Here is a write that describes something very similar to that. The Bayes approach is the right way to proceed.

View answer
Maximizing probability of winning on loaded coin
1 votes

A 75% win probability is definitely a good edge to have. To answer your question, yes it is possible to generalize for n: 100 - n. The Kelly criteria is a good way to go, but as is pointed out in this ...

View answer
Is a Bayesian Classifier a good approach for text with numerical meta-data?
1 votes

You can use numerical values quite easily. In the term P(Feature|scam=Yes) you could put a gaussian distribution or any other empirical distribution from training data (for e.g. sort the data, create ...

View answer
Statistics and Probability interview questions
1 votes

What you ask is not exactly a question. I can point you to this blog which has a few questions worked out. HTH

View answer
Can logistic regression's predicted probability be interpreted as the confidence in the classification
1 votes

If a classifier predicts a certain class with a probability, that number can be used as a proxy for the degree of confidence in that classification. Not to be confused with confidence intervals. For ...

View answer
Combining probabilities in case of incomplete information
0 votes

If the original models did not consider conditional probabilities you are only left with the option of treating both these probabilities as independent. $ P(not cancel) = 1 - P(cancel) $. Here $P(...

View answer
Roadmap to Boltzmann Machines
0 votes

Reading up books on neuron models is a good start, but it may not be the best way to start your journey to be an expert in probability and statistics. Some of the concepts in that area may leave you ...

View answer
Supervised Pattern Recognition with Probabilistic Labels
0 votes

You can try casting the problem as a regression problem, wherein you are trying to predict the probability score. Or perhaps something simpler like replicating more training instances for cases where ...

View answer
Texts on Various Topics in Statistics (GLMs, MCMC, Decision Trees, etc.)
0 votes

Here is a good list to learn the art of probability & statistics. Here is another set to learn monte carlo methods. Note, you are better off getting a good grounding in statistics and probability ...

View answer
odds ratio: the purpose and interpretation
0 votes

Odds ratio for a success rate of $p$ is defined as $\frac{p}{1-p}$. A nice feature of this ratio is the plain English explanation you can do with it in a betting scenario. As an example, assume $p = 0....

View answer
Textbooks pertaining to creating models?
0 votes

I liked the book "The practise of Business Statistics" as a good verbose introduction to the application of creating models with some real world data with real world problems. The mathematics in the ...

View answer
Good references for time series?
0 votes

Here is a good list of books on time series analysis. Note that there is a lot of difference amongst books that cater to people of different backgrounds (economists/engineers/statisticians). hth

View answer
Easy book to understanding basic concepts
0 votes

Here is a good collection of books in probability and statistics including R programming. Stay away from "dummies" or "idiots" guide type books because (I think) they needlessly dumb down concepts in ...

View answer
Self-Study Plan for Becoming Statistical Analyst?
0 votes

Learn R. Absolutely, definitely do so. R is open source and I think will soon become the de facto standard for the statistics & machine learning. You also need to learn a scripting language and I ...

View answer
Any suggestions for a good undergraduate introductory textbook to statistics?
0 votes

Here is a list of books. Puzzles/riddles are a great way to instil an interest in what mathematics/statistics can do. Real life examples help too.

View answer
Statistical comparison of two means with a range not starting at 0
0 votes

The t-test is apt here. There is no dependency on the range involving zero. So long as $t = \frac{\delta \mu}{s}$ is within range (decided by the chosen p-value) for the given number of degrees of ...

View answer
Programmer looking to break into machine learning field
0 votes

Very nice question. A thing to realize upfront is that machine learning is both an art and science and involves meticulously cleaning out data, visualizing it and eventually build models that suite ...

View answer
Reference Request: Generalized Linear Models
-1 votes

Here is a good write up on generalized linear regression. The code is done in R and it explains how they work. CRAN also has a package glmnet which does this for you but can be a bit unwieldy to use ...

View answer
What is a good paper or book to understand standardization and normalization of data with different units of measurement?
-1 votes

You are mostly on the right track here. Standardization is generally a good practice because lots of simple mainstream approaches you would use (like linear regression) assumes you have a normal ...

View answer
Books for learning non parametric Bayesian model
-1 votes

Here is a good collection to buy. I like the "Bundle of algorithms in Java", it gives straight out implementations/examples as does "Machine learning, practical tools and techniques" which is also a ...

View answer