I was looking at Uber Price Estimates API at here. They didn't mention if the returned price estimate is real time estimate or not? Does anyone have any ideas? Thanks!
Yes, the endpoint at the documentation you are referencing is real-time estimate.
Related
Alright, so I am trying to rework the Github contribution graph feature, and I would like the "level" of contribution to be accurate to Github's. By level I mean the brightness of the square (if you are in darkmode).
Here in this image you can see a day with a high level and a day with a low level.
For starters, the level is calculated in relation to the other days, however it is unclear how that calculation is being done. On the Github Docs it says that the level is calculated based on which quartile the day falls into.
I don't know much about statistics, but shouldn't there be an equal amount of days in each quartile? But, if you look at this contribution graph, you can clearly see there are many more days with a lower level than there are with a higher level.
Is there something I am missing? Am I wrong about quartiles? Any help is appreciated.
I'm wondering what is the limit of Bing Distance Matrix API. From the docs here said 2,500 origins-destination pairs. Is it the maximum request per single request or the cumulative monthly limit? Another thing that confused me is about their billable transaction. What is it about the billable transaction?
I did try reading the docs and implementing the API. I'm a little afraid to try a 50x50 matrix as I will get charged.
That limit is per request. There is no monthly limit.
Non-billable transactions are tracked, but don't count towards the free limits (or paid for transaction credits). Billable transactions eat up the free credits and potentially into purchases transaction credits, if you have any.
Note that with Bing maps you have to purchase transactions before you can use them. So, if you don't have a license today, you can at most only max out the free limits. Once you hit the free limits, the service will stop working for your account until you decide to get a paid license, or until a new year has come around. So, you don't need to worry about a surprise bill appearing in this case.
In Bing Maps, every 4 cells in a matrix (origin/destination pair) generates 1 transaction. So, a 50x50 matrix would have 2,500 cells, and thus use 625 transactions. The Bing Maps free limits is 125,000 transactions a year. So, you could in theory run request a 50x50 matrix every other day and stay under the free yearly limit.
I am trying to find time series outlier using Tableau forecast. I need to compare the actual value with the 95% confidence level in forecast results to determine if it is an outlier.
I understand I can view the forecast results on the chart. But I want to use the forecast results in calculated measure. Is there any way to do it? I cannot find any Tableau functions to retrieve the forecast results.
Xuefei. Doesn't look like there is a way currently, at least going by their help page - https://help.tableau.com/v2019.1/pro/desktop/en-us/forecast_options.htm. If you haven't already considered this - integration with R is easy and that way you could just model it in R (accounting for additive/multiplicative, trend/cyclicity/seasonality) and access the forecast values from R. Integration with Python is also supposed to be easy, although I haven't tried it myself.
Example of code in Tableau to incorporate R code for linear regression (this is the formula for the calc field in Tableau)
SCRIPT_REAL("
fv=log(.arg1)
fpri=.arg2
fit=lm(fv~fpri)
exp(fit$fitted)",SUM([Impressions]),SUM([CPM]))
I am attempting to cluster a group of customers based on spend, order frequency, order breadth and what % of purchases they make in each category (there are around 20).
It will probably be a simple answer but I cannot figure out whether I should standardize (subtract mean and divide by sd) the % category buy columns or not. When I dont standardize I can get around 90% of the variance explained in 4-5 principal components (using SVD), but when I standardize each column I only get around 40% for the same number of principal components. My worry is that because each column is related, I am removing the relationship by standardizing. At the same time I am worried that not standardizing will cause issues with the other variables in the data that I have standardized.
I would assume if others tried clustering in this way they would face a similar issue but I cant seem to find one so it might be that I just dont understand the situation. Thanks for any clarification in advance!
Chris,
Percentage scale has a well defined range and nice properties.
By heuristically scaling these features you usually make things worse.
I've read some papers of Matrix Factorization(Latent Factor Model) in Recommendation System,and I can implement the algorithm.I can get the similar RMSE result like the paper said on the MovieLens dataset.
However I find out that,if I try to generate a top-K(e.g K=10) recommended movies list for every user by rank the predicted rating,it seems that the movies that are thought to be rated high point of all users are the same.
Is that just what it works or I've got something wrong?
This is a known problem in recommendation.
It is sometimes called "Harry Potter" effect - (almost) everybody likes Harry Potter.
So most automated procedures will find out which items are generally popular, and recommend those to the users.
You can either filter out very popular items, or multiply the predicted rating by a factor that is lower the more globally popular an item is.