Machine Learning models’ ultimate goal is making reliable predictions on new, unknown data. With this purpose in mind, we want our algorithm to capture relations in existing data and replicate them among new entries. At the same time, we do not want our algorithm to have, let’s say, prejudices because of the data it trained on.
In the first case, we are trying to reduce the bias of our model, which is the difference between the average prediction and the actual value. In the second case, we are trying to reduce the variance, which captures the fluctuations of predictions around the mean value. Both the measurements concur in increasing the error of our model, but, unfortunately, they are negatively correlated: you cannot decrease the one without increasing the other.
A learning curve is the plot of the training/cross-validation error versus the sample size. The learning curve can be used to detect whether the model has the high bias (underfitting) or high variance (overfitting). If the model suffers from high bias problem, as the sample size increases, training error will increase and the cross validation error will decrease.
If the model suffers from overfitting, more samples will help to improve the model prediction performance if the model suffer from high variance.
http://www.holehouse.org/mlclass/10_Advice_for_applying_machine_learning.html
see also learning curves
print(df[df.post.str.contains(‘python’,regex=False)])
post … relkw
4 trying to calculate and print the mean and its… … python
140 why doesn t python spot errors before executio… … python
143 call a python class from command line i have … … python
178 reading constants values from python file and … … python
179 converting user nickname to formal first name … … python
182 unable to assign values to variables in python… … python
240 python: wrong assignment of default argument i… … python
252 how do i do something to each element in a lis… … python
263 converting a dict into a list i have
“A model is done when nothing else can be taken out.” — Dyson
Breitschblog 
So where do our beliefs come in? They come in through the prior, P(A), which in our example is P(Offer) — this is our prior belief about how likely it is to receive an offer. In our example, you can think of the prior as our belief of the likelihood that you will receive an offer at the exact moment that you exit the interview room.
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iserv:= WMIConnect(WMIStart,’localhost’,”,”);
wmi_dom:= ‘Win32_process’;
iset:= WMIExecQuery(iserv,’Select * from ‘+wmi_dom+’ WHERE Name = “maXbox4.exe”‘)
if WMIRowFindFirst(iset, ENum, vObj) then
repeat
PrintF(‘PriorityLevelID of maXbox: %s ‘,[vObj.Priority]);
//vObj.setPriority(‘128’)
until not WMIRowFindNext(ENum, vObj);
vObj:= unassigned;
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Unlike a forward proxy, which is an intermediary for its associated clients to contact any server, a reverse proxy is an intermediary for its associated servers to be contacted by any client. In other words, a proxy acts on behalf of the client(s), while a reverse proxy acts on behalf of the server(s).
An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once. For example, the word anagram can be rearranged into nag a ram, or the word binary into brainy or the word adobe into abode.Wikipedia
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