Error Ds 5.5 Meaning

[Salvador Baltimore]

In the case of a REST API with a JSON payload, 400's are typically, and correctly I would say, used to indicate that the JSON is invalid in some way according to the API specification for the service.

Imagine instead this were XML rather than JSON. In both cases, the XML would never pass schema validation--either because of an undefined element or an improper element value. That would be a bad request. Same deal here.

In neither case is the "syntax malformed". It's the semantics that are wrong. Hence, IMHO a 400 is inappropriate. Instead, it would be appropriate to return a 200 along with some kind of error object such as "error": "message": "Unknown request keyword" or whatever.

Consider the client processing path(s). An error in syntax (e.g. invalid JSON) is an error in the logic of the program, in other words a bug of some sort, and should be handled accordingly, in a way similar to a 403, say; in other words, something bad has gone wrong.

An error in a parameter value, on the other hand, is an error of semantics, perhaps due to say poorly validated user input. It is not an HTTP error (although I suppose it could be a 422). The processing path would be different.

For instance, in jQuery, I would prefer not to have to write a single error handler that deals with both things like 500 and some app-specific semantic error. Other frameworks, Ember for one, also treat HTTP errors like 400s and 500s identically as big fat failures, requiring the programmer to detect what's going on and branch depending on whether it's a "real" error or not.

As a client app, you expect to know if something goes wrong on the server side. If the server needs to throw an error when blah is missing or the requestedResource value is incorrect than a 400 error would be appropriate.

200 doesn't express any information about Expected errors or Handled Exceptions - because that is not part of the Message Transport process. These are status codes about HTTP, the Status of the Transport itself.

No matter what you choose for HTTP response code, it seems everyone agrees your response should explain Why it failed, and what to do to resolve it. In the case of Roman, maybe return a list of acceptable values for the field?

Anyway, as others have illustrated, the error is because the server could not recognize the request cause malformed syntax, I'm just raising a instance at practice. Hope it would be helpful to someone.

Hi! I have a workflow that I created to combine some files. It has six components to them that all use a directory to pull from. They all pull basically the same information but from different months. 5 out of 6 of them work perfectly but I keep getting an error on one. I have never seen this error before. Can you please tell me what this error means?
"Record #3: Tool #1: Cell name (#N/A) is out of range. File may be corrupt."

That was my first thought so I did try that already (I went into each of the three files in this directory and deleted all blank rows and columns at the end to be sure) and I still get the error. I have tried saving the workflow, closing and reopening, deleting that container and recreating, resaving the files from the source onto my desktop. I am at a loss.

Yep, so the configuration is FullPath (V_WString) as are all the others in the workflow. Each directory will pull three files to combine for different quarters. They are all pulling files as below. Each file has the same data (22 columns each, all labeled the same and all columns have numeric or alphanumeric data just different months worth of data). They are all set up as *xlsx for the directory input and the output tool in each container. The same file it references in the macro error, works in another workflow that I had created but I am hoping I can simplify things with this one.

@syazana Hello. I'm not sure if @TriciaB got a solution, but she may not see this as it's a few months without activity. You may want to post this separately for a faster response. Also, your error may mean something different.

For @TriciaB if you haven't gotten an answer, the error is indicating the issue is in tool #1 inside of the macro itself. Tool #1 is typically the macro input tool. I would investigate the macro input tool (verify it is tool # 1) and see if the data inside contains blank rows or (#N/A) as a value. It may be a text input or a file input so check the data in whichever is set. Also, I see 4 macros in your workflow, but it isn't clear if they are all the same macro. Can you confirm (if you are still having an issue). Thanks!

Thank you, @jdminton! It seems like Alteryx treats the empty columns in my source file as columns with Null values, hence causing the Cell name (#N/A) out of range error. Strangely, it works fine when I run the file one by one when running in Macro canvas, but not in my main workflow.

It is also possible to identify the types of difference by looking at an ( x , y ) \displaystyle (x,y) plot. Quantity difference exists when the average of the X values does not equal the average of the Y values. Allocation difference exists if and only if points reside on both sides of the identity line.[4][5]

The mean absolute error is one of a number of ways of comparing forecasts with their eventual outcomes. Well-established alternatives are the mean absolute scaled error (MASE), mean absolute log error (MALE), and the mean squared error. These all summarize performance in ways that disregard the direction of over- or under- prediction; a measure that does place emphasis on this is the mean signed difference.

Where a prediction model is to be fitted using a selected performance measure, in the sense that the least squares approach is related to the mean squared error, the equivalent for mean absolute error is least absolute deviations.

MAE is not identical to root-mean square error (RMSE), although some researchers report and interpret it that way. The MAE is conceptually simpler and also easier to interpret than RMSE: it is simply the average absolute vertical or horizontal distance between each point in a scatter plot and the Y=X line. In other words, MAE is the average absolute difference between X and Y. Furthermore, each error contributes to MAE in proportion to the absolute value of the error. This is in contrast to RMSE which involves squaring the differences, so that a few large differences will increase the RMSE to a greater degree than the MAE.[4]

At this stage, based on the initial error 'message=An internal Platform error has occurred', I'd advise you to reach out to the Support teams by raising a ticket. They can create an access / impersonation request to deep dive into this issue as it happens and will relay to our Engineering teams if further verification is needed.

The MSE is the second moment (about the origin) of the error, and thus incorporates both the variance of the estimator (how widely spread the estimates are from one data sample to another) and its bias (how far off the average estimated value is from the true value).[citation needed] For an unbiased estimator, the MSE is the variance of the estimator. Like the variance, MSE has the same units of measurement as the square of the quantity being estimated. In an analogy to standard deviation, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation (RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance, known as the standard error.

The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled). In the context of prediction, understanding the prediction interval can also be useful as it provides a range within which a future observation will fall, with a certain probability. The definition of an MSE differs according to whether one is describing a predictor or an estimator.

If a vector of n \displaystyle n predictions is generated from a sample of n \displaystyle n data points on all variables, and Y \displaystyle Y is the vector of observed values of the variable being predicted, with Y ^ \displaystyle \hat Y being the predicted values (e.g. as from a least-squares fit), then the within-sample MSE of the predictor is computed as

The MSE can also be computed on q data points that were not used in estimating the model, either because they were held back for this purpose, or because these data have been newly obtained. Within this process, known as cross-validation, the MSE is often called the test MSE,[4] and is computed as

This definition depends on the unknown parameter, but the MSE is a priori a property of an estimator. The MSE could be a function of unknown parameters, in which case any estimator of the MSE based on estimates of these parameters would be a function of the data (and thus a random variable). If the estimator θ ^ \displaystyle \hat \theta is derived as a sample statistic and is used to estimate some population parameter, then the expectation is with respect to the sampling distribution of the sample statistic.

The MSE can be written as the sum of the variance of the estimator and the squared bias of the estimator, providing a useful way to calculate the MSE and implying that in the case of unbiased estimators, the MSE and variance are equivalent.[5]

In regression analysis, "mean squared error", often referred to as mean squared prediction error or "out-of-sample mean squared error", can also refer to the mean value of the squared deviations of the predictions from the true values, over an out-of-sample test space, generated by a model estimated over a particular sample space. This also is a known, computed quantity, and it varies by sample and by out-of-sample test space.

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