i While reading machine learning articles , I came across the following terms:
train set
test set
validation set
query set
support set
sample set
I am really confused .
is validation set and query set the same?
The training data is composed of train and test examples, alternately referred to as the support and query set.
Validation sets are used for parameter tuning.
Further read: https://meta-learning.fastforwardlabs.com/
Related
I am working on a project that uses Scopus API to get document names or journal names under different scenarios. I am using ScopusSearch API (https://dev.elsevier.com/documentation/ScopusSearchAPI.wadl) and SerialTitle API (https://dev.elsevier.com/documentation/SerialTitleAPI.wadl) for the purpose.
However, the total number of documents I am able to retrieve using these API's is very few. I want to increase the number of documents being fetched. Now, I've been through the documentation of these API's a several times but I am confused with the use of start parameter and the cursor parameter.
Take for example, ScopusSearch API, under its query params section:
start parameter
cursor parameter
Can someone please help me understand the difference between these two? And more specifically when to use the start and when to use the cursor parameter?
If you use pybliometrics, as your tag suggests, then you don't need to care about this.
The basic idea behind this pagination (that's what you're after) is:
Run a query with unlimited number of results with cursor set to "*"
Set start to 0 and get the first count results
Set start to start+count+1 and get the next count results
Repeat step 3 until all results are fetched
How can I set more rules for setting up value after changing a record?
I am used to use rule Set or calculate value but ObjectGears allow just one rule for this type. Depending on values from other columns I want to set naother columns in various way.
You should choose Action type: Start a script. You define a single script with a cascade of conditions and value settings.
You can find a good example here:
https://doc.objectgears.cz/vcd/en-US/og_examples_setting_up_record_value
I'm trying to build a logistic model and I have already divided the training and validation data sets. I used the SCORE statement in order to validate the model against the validation data. In reviewing the SAS documentation, I read the following: "Similarly, an actual by predicted table can be created for a validation data set by using the SCORE statement which also produces a data set containing predicted probability variables and a variable (I_y, where y is the name of your response variable) containing the predicted response category. Note that the validation data set must contain the observed responses in order to produce the table." However my code does not produce the actual by predicted table.
I have also tried an OUTMODEL and INMODEL code with similar results.
proc logistic data=train plots(only)=(effect oddsratio);
class Gender Geography;
model Exited(event="1") = &cat &interval / selection=stepwise clodds=pl slstay=.05 slentry=.05;
score data=valid out=churn.churn_pred_sw;
run;
The only warning that I receive is as follows: WARNING: Some plots have more than 5000 observations and are suppressed. Specify the PLOTS(MAXPOINTS=NONE) option in the PROC
LOGISTIC statement to display the plots.
If I remove the Plots statement, it resolves this issue but still does not produce the actual vs predicted table based on the validation set.
In GBM Model - I have near to 150 columns used to train and create a model - I have a case where for some records I won't be getting all the columns. In that case will the model work - I don't want to set the values to 0 in that case.?
Your question title and description are talking about 2 different things and title is not clear about what you are asking. My following answer is based on your question in description field:
If you use H2O to build your GBM model H2O replaces missing numerical, categorical & unseen values to NA. Please look at the following documentation regarding "handling missing values in GBM" which will help you understand more about your case:
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm-faq/missing_values.html?highlight=missing%20values
I have a data cube which is based on a data set. The data set takes a number of parameters. So far I have not been able to find out how to pass parameters to that data set. I see that it is possible to set a filter on the data cube but that doesn't help me.
How can I pass the necessary parameters to the data set?
To pass parameters to a relational data source in BIRT:
Set up appropriate Report Parameters (via the Data Explorer).
Add parameter markers (ie. ?s) to your SQL query where you want to parameterise the query (eg. if you want to select customers by country, change your datasource query to be SELECT * FROM CUSTOMER WHERE COUNTRY = ? ).
Add dataset parameters to your dataset (via the Parameters tab of the Edit Data Set dialog) in the order they appear in your SQL query, and specify the corresponding Report Parameter in the Linked to Report Parameter value of the New Parameter dialog. (This is how you manually bind the Report Parameters to the parameters in the query - you will need to set up a dataset parameter for each parameter marker in the SQL query.)
If you now preview the report, it should prompt you for parameter values and then display the report (appropriately filtered).
If you were using a non-relational data source, you would set up a filter at the dataset level instead of parameterising the query - there is an example of this approach here. (You can do this with SQL-based reports too, but it is less efficient than filtering at the query level.)