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 ...
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 ...
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 ...
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 ...
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:
Additionally you can save and load a pipeline object by joblib.dump and jublib.load.
Your WEBS_4 is an array with two elements, so there's no ip element in it, which explains why this fails:
data->'results'->'WEBS'->'WEBS_4' ->'ip' as test
You need to get the zeroth element of WEBS_4, and then you can get the ip element of that. This works in a ...
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 ...
You can refer below the answer to the similar question asked.
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.
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.
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.
This is a late answer. There are syntax examples elsewhere, but, for the sake of completeness, the syntax should be as follows:
laf <- laf_open_fwf("foo.dat",
column_widths = colWidths,
column_names = colNames)
castData <- laf[laf$...