adaboost | Adaboost implementation using bayesian and decision tree | Machine Learning library

The documentation states that the strings used to identify estimators and preprocessors are the filenames without .py.

You can find here the model_id you are looking for here.

From the documentation MLP code is mlp, and Logistic Regression is not implemented. (see this issue for further information)

Therefore you should do as follows:

I think is due the nature of arguments and options, you are mixing arguments and options although is not implicit stated in the documentation but argument will eat up the options that follow. If nargs is not allocated it will default to 1 considering everything after it follows as string which it looks like this is the case.

nargs – the number of arguments to match. If not 1 the return value is a tuple instead of single value. The default for nargs is 1 (except if the type is a tuple, then it’s the arity of the tuple).

I think you should first use options followed by the argument as display on the documentation page. Other approach is to group it under a command as show on this link.

First, I would suggest to save all information into lists, so the plot can be made via a large loop. That way, if some detail changes, it only needs to be changed at one spot.

To create a legend, graphical elements that have a "label" will be added automatically. Normally, a complete bar plot only gets one label. By diving into the generated bars, individual labels can be assigned.

The code first creates a dummy legend, so fig.tight_layout() can adapt all the spacings and leave some place for the legend. After calling fig.tight_layout(), the real legend is created. (With the real legend, fig.tight_layout() would try to assign it completely to one subplot, and create a wide gap between the two columns of subplots).

Is this close to what you're looking for ?

Does this serve your purpose?

Yes, there are ways like Univariate, Bivariate and Multivariate analysis to look at the data and then decide which model you can start as baseline.

you can also use the sklearn way to choose the right estimator.

https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

No wonder you have not actually seen anyone doing it - it is an absurd and bad idea.

You are trying to build an ensemble (Adaboost) which in itself consists of ensemble base classifiers (RFs) - essentially an "ensemble-squared"; so, no wonder about the high computation time.

But even if it was practical, there are good theoretical reasons not to do it; quoting from my own answer in Execution time of AdaBoost with SVM base classifier:

Adaboost (and similar ensemble methods) were conceived using decision trees as base classifiers (more specifically, decision stumps, i.e. DTs with a depth of only 1); there is good reason why still today, if you don't specify explicitly the base_classifier argument, it assumes a value of DecisionTreeClassifier(max_depth=1). DTs are suitable for such ensembling because they are essentially unstable classifiers, which is not the case with SVMs, hence the latter are not expected to offer much when used as base classifiers.

On top of this, SVMs are computationally much more expensive than decision trees (let alone decision stumps), which is the reason for the long processing times you have observed.

The argument holds for RFs, too - they are not unstable classifiers, hence there is not any reason to actually expect performance improvements when using them as base classifiers for boosting algorithms, like Adaboost.

You haven't done anything wrong. The classifier sets a new random state every time you run it. To fix that just set the random_state parameter to any value you like.

Eg:

this code, make a rank for every feature