AdTree | Accurate, detailed, and automatic modelling of laser-scanned trees | Build Tool library

I have turned your code snippet into one with imports and fixed the MultiSearch setup for Bagging (mparam.prop = "numIterations" instead of mparam.prop = "numOfBoostingIterations"), allowing it to be executed.

Since I do not have access to your data, I just used the UCI dataset vote.arff.

Your code was a bit odd, as it did a 70/30 train/test split, trained the classifier and then performed cross-validation on the test data. For cross-validation you do not train the classifier, as this happens within the internal cross-validation loop (each trained classifier inside that loop gets discarded, as cross-validation is only used for gathering statistics).

The code below has therefore three parts:

1. your original evaluation code, but commented out
2. performing proper cross-validation
3. performing train/test evaluation

I do not use Jupyter notebooks and tested the code successfully in a regular virtual environment on my Linux Mint:

• Python: 3.8.10
• Output of pip freeze:

You need to instantiate ASSearch and ASEvaluation objects. If you have command-lines, you can use the from_commandline helper method like this:

python-weka-wrapper3, as the name suggests, is a light-weight wrapper around Weka classes. However, it mostly only wraps abstract superclasses, not all of the thousands of classes that make up Weka.

The weka.classifiers.Classifier class is the wrapper to use for classifiers and you need to specify the Java classname of the actual classifier that you want to wrap (in your case weka.classifiers.trees.ADTree). And, yes, just like Python, Java is also case sensitive.

Furthermore, if you require classes from packages, then you need to start the JVM with package support (default is no package support).

Below is an example that outputs the command-line of ADTree with its default parameters. If necessary, it installs the package first.

In order to do that you have to open Weka and Select Tools > Package Manager. On the "Package Manager" go to "Package Search" and look for:

1. "bestFirstTree" and
2. "AlternatingDecisionTrees" (Classification) or
3. "AlternatingModelTrees" (Regression).

Don't forget to select it (so it becomes blue) and then click Install !

It was enough to change the name of the column.

you don't need a switch. if you wanna access your properties by name, you can create an Expression to build a Func

I've managed to work out a way to do it using a lambda expression.