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I'd suggest you go straight to R or Python, or whatever, rather than use Excel or an open source alternative. You will be very limited in what you can do with a spreadsheet compared to a proper programming environment and you will probably very quickly abandon the spreadsheet, so why waste the time in the first place? Although going straight to a ...


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I tool like MS Excel or Libre Office Calc (open source) is nice to view data in a table and has a low barrier to entry - after playing around for 30 minutes, you can probably get most basic tasks done. Using a programming language like R or Python opens up many many other opportunities for more advanced analysis. People write packages that will do a lot of ...


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I mean most of the time it's very much about preference really. If you want any indication on when to use what: MS-Excel can only handle so much data. Good for quick analysis of small data but neither good for production or middle to large amounts of data R great tool for calculating stuff, running models, etc. Pre-Processing is good, lots of libraries to ...


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One way to do this is to convert the source code from Python to R - see line 289 here. You could also check for functionality in existing R packages - for instance, mldr, mlr or caret packages.


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For larger projects snakemake is a way to go for Python (it extends Python syntax, valid Python is valid snakemake). It originates in bioinformatics and even has its own publication; it is widley adopted and used by many projects (see the literature list in the first link or the citations for the linked article). For Jupyter notebook based projects, I made ...


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Sklearn has pipeline. If you have fit and transform attributes iteratively, you can make them pipeline by Pipeline class in sklearn.pipeline. Read the docs: https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html Additionally you can save and load a pipeline object by joblib.dump and jublib.load.


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Your WEBS_4 is an array with two elements, so there's no ip element in it, which explains why this fails: dbGetQuery(conexion,"SELECT data->'results'->'WEBS'->'WEBS_4' ->'ip' as test FROM results;") You need to get the zeroth element of WEBS_4, and then you can get the ip element of that. This works in a ...


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It's hard to say, without being able to know exactly what Azure is doing. From what they do share, they bin continuous features; you could try tree_method='hist' in xgb to be more similar there. I can't tell how Azure deals with categoricals or missing values. Be sure to set xgb's max_depth=0 and grow_policy='lossguide', since you want to use max_leaves ...


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You can refer below the answer to the similar question asked. https://stackoverflow.com/questions/14096520/compile-r-script-into-standalone-exe-file Alternatively, You can use R-shiny app for it. You can build application whih can be deployed on website also. See below link for the examples of application made by R-shiny. https://shiny.rstudio.com/gallery/...


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As you have 2 numbers, your network has two output nodes. For example, Female and Male. In binary classification the output nodes are independent and the prediction for each node is from 0 to 1. So, you should consider a threshold (usually 0.5). Then if the prediction value is upper than this threshold for Male, you consider the image as Male.


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Logistic regression problem works with atleast two variables. Independent variable - Variable based on which you have to predict your dependent variable. After you have those, you can input these in simple r code. Google glm function in R. you should get your answer. Hope, it helps.


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This is a late answer. There are syntax examples elsewhere, but, for the sake of completeness, the syntax should be as follows: library(LaF) laf <- laf_open_fwf("foo.dat", column_widths = colWidths, column_types=rep("character", length(colWidths)), column_names = colNames) castData <- laf[laf$...


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