I ran 9*6(total of 54) lm regressions similar to y ~ p + u + v +... and now want to format the results of these regressions as one singel html table to be used in word, etc. I am new to making such tables but understand stargazer, huxtable and texreg might be useful. I want my table to include just the coefficient and significance stars of variable p for all 54 regressions, and want the table to look something like this:
[Output][1]
[1]: https://i.stack.imgur.com/RFd6E.png
I have stored the 54 regression model outputs in a list. 9 and 9 of those models will be each of the 6 3x3 matrices in the table.
I am not allowed to share a sample of the dataset, but i believe any kind of regression result is applicable. As a proxy I suggest using the mtcars dataset and running 54 regressions. I belive that to be the simplest solution.
So far I have tried using stargazer, but have not been able to format the table as wanted.
Related
So I have been looking at XGBoost as a place to start with this, however I am not sure the best way to accomplish what I want.
My data is set up something like this
Where every value, whether it be input or output is numerical. The issue I'm facing is that I only have 3 input data points per several output data points.
I have seen that XGBoost has a multi-output regression method, however I am only really seeing it used to predict around 2 outputs per 1 input, whereas my data may have upwards of 50 output points needing to be predicted with only a handful of scalar input features.
I'd appreciate any ideas you may have.
For reference, I've been looking at mainly these two demos (they are the same idea just one is scikit and the other xgboost)
https://machinelearningmastery.com/multi-output-regression-models-with-python/
https://xgboost.readthedocs.io/en/stable/python/examples/multioutput_regression.html
I am trying to evaluate user-corrected AI runs using Metabase. In particular, I have a classification problem with around 100 labels, and would like to create a dashboard showing how often the predicted and user-corrected labels coincide, per label and per 3 months. How do you do this in Metabase, or in SQL in general, if e.g. we have a table with id, predLabel, selectedLabel columns?
I am trying to create a machine learning model to predict the position of each team, but I am having trouble organizing the data in a way the model can train off of it.
I want the pandas dataframe to look something like this
Where each tournament has team members constantly shifting teams.
And based on the inputted teammates, the model makes a prediction on the team's position. Anyone have any suggestions on how I can make a pandas dataframe like this that a model can use as trainnig data? I'm completely stumped. Thanks in advance!
Coming on to the question as to how to create this sheet, you can easily get the data and store in the format you described above. The trick is in how to use it as training data to your model. We need to convert it in numerical form to be able to be used as training data to any model. As we know that the max team size is 3 in most cases, we can divide the three names in three columns (keep the column blank, if there are less than 3 members in the team). Now we can either use Label encoding or One-hot encoding to convert the names to numbers. You should create a combined list of all three columns to fit a LabelEncoder and then use transform function individually on each column (since the names might be shared in these 3 columns). On label encoding, we can easily use tree based models. One-hot encoding might lead to curse of dimensionality as there will be many names, so I would prefer not to use it for an initial simple model.
I am being asked to take a look at a scenario where a company has many projects that they wish to complete, but with any company budget comes into play. There is a Y value of a predefined score, with multiple X inputs. There are also 3 main constraints of Capital Costs, Expense Cost and Time for Completion in Months.
The ask is could an algorithmic approach be used to optimize which projects should be done for the year given the 3 constraints. The approach also should give different results if the constraint values change. The suggested method is multiple regression. Though I have looked into different approaches in detail. I would like to ask the wider community, if anyone has dealt with a similar problem, and what approaches have you used.
Fisrt thing we should understood, a conclution of something is not base on one argument.
this is from communication theory, that every human make a frame of knowledge (understanding conclution), where the frame construct from many piece of knowledge / information).
the concequence is we cannot use single linear regression in math to create a ML / DL system.
at least we should use two different variabel to make a sub conclution. if we push to use single variable with use linear regression (y=mx+c). it's similar to push computer predict something with low accuration. what ever optimization method that you pick...it's still low accuracy..., why...because linear regresion if you use in real life, it similar with predict 'habbit' base on data, not calculating the real condition.
that's means...., we should use multiple linear regression (y=m1x1+m2x2+ ... + c) to calculate anything in order to make computer understood / have conclution / create model of regression. but, not so simple like it. because of computer try to make a conclution from data that have multiple character / varians ... you must classified the data and the conclution.
for an example, try to make computer understood phitagoras.
we know that phitagoras formula is c=((a^2)+(b^2))^(1/2), and we want our computer can make prediction the phitagoras side (c) from two input values (a and b). so to do that, we should make a model or a mutiple linear regresion formula of phitagoras.
step 1 of course we should make a multi character data of phitagoras.
this is an example
a b c
3 4 5
8 6 10
3 14 etc..., try put 10 until 20 data
try to make a conclution of regression formula with multiple regression to predic the c base on a and b values.
you will found that some data have high accuration (higher than 98%) for some value and some value is not to accurate (under 90%). example a=3 and b=14 or b=15, will give low accuration result (under 90%).
so you must make and optimization....but how to do it...
I know many method to optimize, but i found in manual way, if I exclude the data that giving low accuracy result and put them in different group then, recalculate again to the data group that excluded, i will get more significant result. do again...until you reach the accuracy target that you want.
each group data, that have a new regression, is a new class.
means i will have several multiple regression base on data that i input (the regression come from each group of data / class) and the accuracy is really high, 99% - 99.99%.
and with the several class, the regresion have a fuction as a 'label' of the class, this is what happens in the backgroud of the automation computation. but with many module, the user of the module, feel put 'string' object as label, but the truth is, the string object binding to a regresion that constructed as label.
with some conditional parameter you can get the good ML with minimum number of data train.
try it on excel / libreoffice before step more further...
try to follow the tutorial from this video
and implement it in simple data that easy to construct in excel, like pythagoras.
so the answer is yes...the multiple regression is the best approach for optimization.
I want to compute means with bootstrap confidence intervals for some subsets of a dataframe; the ultimate goal is to produce bar graphs of the means with bootstrap confidence intervals as the error bars. My data frame looks like this:
ATG12 Norm ATG5 Norm ATG7 Norm Cancer Stage
5.55 4.99 8.99 IIA
4.87 5.77 8.88 IIA
5.98 7.88 8.34 IIC
The subsets I'm interested in are every combination of Norm columns and cancer stage. I've managed to produce a table of means using:
df.groupby('Cancer Stage')['ATG12 Norm', 'ATG5 Norm', 'ATG7 Norm'].mean()
But I need to compute bootstrap confidence intervals to use as error bars for each of those means using the approach described here: http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
It boils down to:
import scipy
import scikits.bootstraps as bootstraps
CI = bootstrap.ci(data=Series, statfunction=scipy.mean)
# CI[0] and CI[1] are your low and high confidence intervals
I tried to apply this method to each subset of data with a nested-loop script:
for i in data.groupby('Cancer Stage'):
for p in i.columns[1:3]: # PROBLEM!!
Series = i[p]
print p
print Series.mean()
ci = bootstrap.ci(data=Series, statfunction=scipy.mean)
Which produced an error message
AttributeError: 'tuple' object has no attribute called 'columns'
Not knowing what "tuples" are, I have some reading to do but I'm worried that my current approach of nested for loops will leave me with some kind of data structure I won't be able to easily plot from. I'm new to Pandas so I wouldn't be surprised to find there's a simpler, easier way to produce the data I'm trying to graph. Any and all help will be very much appreciated.
The way you iterate over the groupby-object is wrong! When you use groupby(), your data frame is sliced along the values in your groupby-column(s), together with these values as group names, forming a so-called "tuple":
(name, dataforgroup). The correct recipe for iterating over groupby-objects is
for name, group in data.groupby('Cancer Stage'):
print name
for p in group.columns[0:3]:
...
Please read more about the groupby-functionality of pandas here and go through the python-reference in order to understand what tuples are!
Grouping data frames and applying a function is essentially done in one statement, using the apply-functionality of pandas:
cols=data.columns[0:2]
for col in columns:
print data.groupby('Cancer Stage')[col].apply(lambda x:bootstrap.ci(data=x, statfunction=scipy.mean))
does everything you need in one line, and produces a (nicely plotable) series for you
EDIT:
I toyed around with a data frame object I created myself:
df = pd.DataFrame({'A':range(24), 'B':list('aabb') * 6, 'C':range(15,39)})
for col in ['A', 'C']:
print df.groupby('B')[col].apply(lambda x:bootstrap.ci(data=x.values))
yields two series that look like this:
B
a [6.58333333333, 14.3333333333]
b [8.5, 16.25]
B
a [21.5833333333, 29.3333333333]
b [23.4166666667, 31.25]