Clustering classifiers with sklearn - python

I have let's say 100 base classifiers trained using BaggingClassifier from sklearn. I know I can cluster data using sklearn (eg using K-Means) but it's only for dataset not for classifiers. Can I cluster classifiers (from BaggingClassifier) into clusters? If I cannot do this using sklearn then are there any other techniques to cluster base classifiers?

Related

Sklearn Multi-target regressor/classification

How can I check if a regressor/classifier in sklearn supports Multi-Target output ?
Sklearn documentation says the following about MultiOutputRegressor :
This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

Visualize sklearn stackingclassifier model pipeline construct

With scikit-learn pipeline we can visualize our pipeline construct. See below screenshot.
I couldn't find similar plotting feature for a sklearn stacking classifier. How can I represent the ensemble model construct with sklearn stacking classifier?

Just like voting classifier, StackingClassifier too could be added as a component of the model pipeline as shown below:

3D dataset and sklearn regression algorithms

I have a dataset similar to the image below.
How can I train one of the regression algorithms defined in the sklearn library using this dataset?

How to parallelize the training of an SVC with RBF kernel through MapReduce in scikit-learn for Python

How would I go about splitting up the training of a SVC classifier from the scikit-learn library among multiple processes (or completely separate computers)? I've found several papers that mention the feasibility of training an SVM iteratively in parallel, but I haven't found any specific examples that apply to the scikit-learn library.

Import SVM light file format in NLTK

I am trying to use NLTK for training a Naive Bayes classifier for multi-class text classification. But I do not have access to the original texts. I am provided with is a file in SVM Light format (one instance each line with feature:value pair). I simply have to import this file and train and test Naive Bayes classifier using this dataset. I was wondering if there is some way to import this file into NLTK and use it directly for training classifiers.

According to nltk's own documentation this is achieved something like this:
Excerpt from Documentation:
scikit-learn (http://scikit-learn.org) is a machine learning library
for Python. It supports many classification algorithms, including
SVMs, Naive Bayes, logistic regression (MaxEnt) and decision trees.
This package implement a wrapper around scikit-learn classifiers. To
use this wrapper, construct a scikit-learn estimator object, then use
that to construct a SklearnClassifier. E.g., to wrap a linear SVM with
default settings:
Example:
>>> from sklearn.svm import LinearSVC
>>> from nltk.classify.scikitlearn import SklearnClassifier
>>> classif = SklearnClassifier(LinearSVC())
See: http://www.nltk.org/api/nltk.classify.html#module-nltk.classify.scikitlearn

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