As you mentioned, Orange is a data mining software developed by the University of Ljubljana. It can be used for developing and testing machine learning models as well as conducting exploratory data analysis and visualization.
One of the unique features that makes Orange "special" is its simplicity and ease of use. This is because components in ...
You have to add higher order polynomial terms to your dataset yourself. Accordingly, you should add new columns to your dataset which consists of higher order polynomials with the desired combination of the existing features. It means that if you have already a CSV file which contains two columns for different samples, you have to add your desired extra ...
The error message shows exactly what you need to do:
Orange requires Python>=3.4
You have to specify a Python>=3.4 version (with orange3 itself installed) while installing orange3-prototypes.
I'm not a Mac user, however, in Windows, Orange3 installer will automatically install a Python 3.4 if there's no compatible Python available.
There are three ways to install Orange add-ons:
The Options -> Add-ons menu of Orange.
Use pip: pip3 install orange3-prototypes
Install from the source:
git clone https://github.com/biolab/orange3-prototypes.git
python3 setup.py install
An infrared data set "Collagen spectroscopy" is available in the "Data Sets" widget. There are some other but small data sets in the infrared data folder: the easiest way to find the folder is to search for "peach_juice.dpt".
Orange Infrared can read Opus, Envi, .spa, .nea, Omnic maps, and some ASCII file formats.
No, no special structure is required for text classification problems in comparison with core Orange.
However — and this holds for core Orange as well as for text addon — if you would like to perform classification one feature must be set as class variable. Otherwise Orange cannot know which feature you would like to predict. When you are reading the data ...
I found 3 ways to do it :
you have to use the Data Info widget who gives the number of rows of your dataset and the Box Plot widget who prints the average and the standard deviation of each feature in your dataset. Pro: easy to use. Con: you are unable to use this data.
you can create a Python script who will compute the average and the standard deviation ...
"Realtime" is a very big word, in many regards you would only call a system realtime when there is very little latency in a system, almost no latency. It would require specialized hardware and os, for a discussion of python and realtime see https://stackoverflow.com/questions/7079864/real-time-operating-via-python.
If you describe the requirement as "...
This is because we have to exclude the target column from the second document (testing data-set).
In your example remove the column 'Status' from second file.
In below example, in training dataset, 'Loan_Status' column is present which is the target field, whereas, in testing dataset, 'Loan_Status' column is not present, and will be computed by ...
Meta variables are meta data, data about data, not used for statistical inference.
Features or variables or attributes are the measured inputs of the problem domain, the independent variables. The target variable is the dependent variable or the measure we're trying to model or forecast. Not all problems can be or need to be formulated in such a way. Orange ...
Not by default, no, as shown by the normalise=False here:
fit_intercept=True, normalize=False, copy_X=True, max_iter=None,
tol=0.001, solver='auto', preprocessors=None)
According to the message I assume that the class variable is not set correctly. Can you try with this excel file? I just removed percentage signs and renamed variables otherwise this is your data. First I loaded this as it is and then set the class variable in Orange. You can do this inside File widget by clicking on feature string in the third column of the ...
Test on train data uses the whole data set for training and then for testing. This method practically always gives wrong results.
So, yes, in this case, the model is learned and tested on the same data set. In most cases, the model learned this way will be overfitted and will perform poorly on unseen data (e.g., your testing dataset).
Test on test data: ...
I will give you a simple solution using R which requires the wordcloud package. Of course there are many other solutions which do not require any programming skills.
The solution is a slight varient of this R-Bloggers tutorial. Feel free to have a look there for further formatting.
words = c('Paolo Gentiloni', 'Matteo Renzi',
The easiest way to install Orange's addons is through the application itself. Open Orange, in the menu click Options -> Addons. In the popup window mark Orange3-Prototypes and click OK. Note, that by doing so you will get the latest version that is published on PyPI.
If you would want to install the bleeding edge version directly from GitHub — assuming ...
It appears those numbers are Unix timestamps. The numbers you quote correspond to the following human-readable dates in ISO format:
Epoch date Human readable date (GMT)
1450396800 2015-12-18 00:00:00
1438905600 2015-08-07 00:00:00
1438732800 2015-08-05 00:00:00
You can use an online epoch converter to confirm.
Apparently, Predictions widget treats ...
As r.kfr said: Import Images works with folders, not individual images. Place all images in a folder and select it for import.
Note that if you have images in different folders, Orange will consider each folder name as a class label for images.
Alternatively, you can create an Excel/CSV file and define the path to the image. Mark it with meta type=image in ...
Test on train data gives you good results but when tested against the reality, majority of time it fails as test on train data does the testing on train data itself and results overfitting.
You should not rely on test on train data. To make your model robust to unseen data, I would recommended to use cross validation techniques like k-fold.It incorporates ...
Perhaps I can elaborate a bit on this.
Test&Score is used for evaluating a model. You provide T&S with training data and the learner (e.g. Random Forest) and then the widget performs 10-fold cross-validation on training data, leaving out 1/10 of a data for testing. Results of all 10 fold are then combined into a single evaluation result.
The answer is yes. If Test & Score is given only one data set, then all it can do is show results of cross-validation.
To test the models on a separate data set, use separate File widgets to load training and test data. Connect File widget with training data to Test & Score, and the connect File widget with Test data to Test & Score. The connect ...
Since I was also looking for it just yesterday being a newbie,sharing what I found , As per Orange FAQ it says
and it directs to the link @K3---rnc mentioned.
But i found similar request as well not sure how well it works-
Orange Data Mining load saved models