Saravanan666/pypi-org huggingface dataset:
Package: deciphon-snap
deciphon-snapExamplefromdeciphon_snap.read_snapimportread_snapsnap_file=read_snap("example.dcs")print(snap_file)shape: (470, 7)
┌────────┬────────────┬─────┬─────────────┬─────────────┬─────────────┬───────────────────────────────────┐
│ seq_id │ profile    │ abc │ alt         │ null        │ evalue      │ match_list                        │
├────────┼────────────┼─────┼─────────────┼─────────────┼─────────────┼───────────────────────────────────┤
│ 0      │ PF00004.32 │ dna │ -1849.56006 │ -1850.08313 │ -13.8193617 │ (∅,S,∅,∅) (ATG,N,ATG,M) (CGA,N,C… │
│ 0      │ PF03969.19 │ dna │ -1853.90649 │ -1850.08313 │ -16.1090849 │ (∅,S,∅,∅) (ATG,N,ATG,M) (CGA,N,C… │
│ 0      │ PF00308.21 │ dna │ -1679.86353 │ -1850.08313 │ -193.97191  │ (∅,S,∅,∅) (ATG,N,ATG,M) (CGA,N,C… │
│ 0      │ PF08299.14 │ dna │ -1789.81091 │ -1850.08313 │ -71.2547802 │ (∅,S,∅,∅) (ATG,N,ATG,M) (CGA,N,C… │
│ …      │ …          │ …   │ …           │ …           │ …           │ …                                 │
│ 272    │ PF02371.19 │ dna │ -1385.17554 │ -1422.72034 │ -51.5481154 │ (∅,S,∅,∅) (ATG,N,ATG,M) (CAC,N,C… │
│ 295    │ PF01170.21 │ dna │ -2005.65234 │ -2003.75269 │ -12.6652124 │ (∅,S,∅,∅) (ATG,N,ATG,M) (TTC,N,T… │
│ 300    │ PF00750.22 │ dna │ -2282.74487 │ -2298.92017 │ -48.1403459 │ (∅,S,∅,∅) (ATG,N,ATG,M) (TTT,N,T… │
│ 302    │ PF13462.9  │ dna │ -855.013794 │ -853.616028 │ -16.6152838 │ (∅,S,∅,∅) (ATG,N,ATG,M) (AAA,N,A… │
└────────┴────────────┴─────┴─────────────┴─────────────┴─────────────┴───────────────────────────────────┘
Package: deciplot
DeciplotThis is a handy tool that helps you better visualize your data by plotting 2D decision boundaries, using any sklearn classifier models.InstallationRun the following to install:pipinstalldeciplotUsagefromsklearn.datasetsimportload_irisfromsklearn.treeimportDecisionTreeClassifierfromdeciplotimportDeciPlot2D# Get iris dataset from sklearniris=load_iris()X,y,feature_names,target_names=iris['data'],iris['target'],iris['feature_names'],iris['target_names']# Initialize a classifier model for plotting decision boundariesdtree=DecisionTreeClassifier()# Initialize an DecPlot2D objectdp2d=DeciPlot2D(X,y,feature_names,target_names,dtree)# Plot and visualize decision boundariesdp2d.plot(figsize=(20,10))After running this code, we'll get this beautiful plot:
Package: decisao-diretoria-363
Decisão de Diretoria CETESB 363/11Por meio daDecisão de Diretoria da CETESB nº 363 de 07.12.2011, que"dispõe sobre a adoção do parâmetro E.coli, para avaliação da qualidade
dos corpos de águas do território do Estado de São Paulo", é adotado o
parâmetroEscherichia coliem substituição aos Coliformes Termotolerantes,
estabelecidos pelas Resoluções CONAMA nº 357/2005, de 17.03.2005, e 274/2000,
de 29.11.2000.ObjetivoO projeto objetiva disponibilizar os parâmetros de qualidade em formato adequado para utilização em análises computacionais.Como Instalar?pip3installdecisao-diretoria-363--upgradeComo Usar?fromnormasimportdecisao_diretoria_363# Get Tabledf_363,list_classes=decisao_diretoria_363.get_parameters()# Filter Data by "Classe"df_363,list_parametros=decisao_diretoria_363.filter_by_classe(df_363,classe='Classe 3')# Filter Data by "Parâmetro", quando tem condições distintas!dict_363=decisao_diretoria_363.filter_by_parameters(df_363,parametro='Escherichia coli',condicao=1)print(dict_363)TestesCaso queira testar, segue umGoogle Colab.TODO...
Package: decision
decisionIn what followspythonis an alias forpython3.6orpypy3.6or any later version (python3.7,pypy3.7and so on).InstallationInstall the latestpip&setuptoolspackages versionspython-mpipinstall--upgradepipsetuptoolsUserDownload and install the latest stable version fromPyPIrepositorypython-mpipinstall--upgradedecisionDeveloperDownload the latest version fromGitHubrepositorygitclonehttps://github.com/lycantropos/decision.gitcddecisionInstallpythonsetup.pyinstallUsage>>>fromdecision.partitionimportcoin_change>>>coin_change(10,[2])(2,2,2,2,2)>>>coin_change(10,[2,3])(2,2,3,3)>>>coin_change(10,[2,3,4])(2,4,4)>>>coin_change(10,[2,3,4,5])(5,5)DevelopmentBumping versionPreparationInstallbump2version.Pre-releaseChoose which version number category to bump followingsemver
specification.Test bumping versionbump2version--dry-run--verbose$CATEGORYwhere$CATEGORYis the target version number category name, possible
values arepatch/minor/major.Bump versionbump2version--verbose$CATEGORYThis will set version tomajor.minor.patch-alpha.ReleaseTest bumping versionbump2version--dry-run--verbosereleaseBump versionbump2version--verbosereleaseThis will set version tomajor.minor.patch.Running testsInstall dependenciespython-mpipinstall-rrequirements-tests.txtPlainpytestInsideDockercontainer:withCPythondocker-compose--filedocker-compose.cpython.ymlupwithPyPydocker-compose--filedocker-compose.pypy.ymlupBashscript:withCPython./run-tests.shor./run-tests.shcpythonwithPyPy./run-tests.shpypyPowerShellscript:withCPython.\run-tests.ps1or.\run-tests.ps1cpythonwithPyPy.\run-tests.ps1pypy
Package: decision-boundary-mapper
Short DescriptionThis is the package provides functionality for visualizing the classifiers decision boundaries.It is based on the work of Cristian Grosu for the master thesis project for 2023 at Utrecht University.
If you use this package, please cite the following paper:[Decision Boundary Maps for Supporting User-Driven Pseudo-labeling](https://drive.google.com/file/d/12PlvWEYmDpdhF0HefwSeEEbx4VUFtDGA/view?usp=sharing)The package is available on PyPI and can be installed using pip:pip install decision-boundary-mapperDocumentationSee more details athttps://decisionboundarymapper.000webhostapp.com/Usage exmaplesThis package comes with a simple GUI that allows you to visualize the decision boundaries of a classifier. The GUI is based on thePySimpleGUIpackage and can be started by running the following code:fromdecision_boundary_mapperimportGUIGUI().start()The package comes with two examples of complete pipelines for visualizing the decision boundaries of a classifier.
Both examples useMNIST(handwritten digits) dataset.
The first exampleDBM_usage_exampleusest-SNEto project the data from thenDspace to the2Dspace, then neural network is trained to fit the inverse projection from2DtonDand the decision boundaries are visualized using the2Dprojection. The second exampleSDBM_usage_exampleuses a neural network with an autoencoder architecture to learn the projection and the inverse projection. After which a simple classifier is used to color each point of the2Dprojection.
The examples can be found in theexamplesfolder.fromdecision_boundary_mapperimportDBM_usage_example,SDBM_usage_exampleDBM_usage_example()# run the first exampleSDBM_usage_example()# run the second exampleThe package main functionality comes in two classesDBM(i.e. learns inverse projection when a 2D projection is given) andSDBM(i.e. learns both the projection and the inverse projection).
The classes can be used as follows:fromdecision_boundary_mapperimportDBM,SDBMfrommatplotlibimportpyplotasplt# load the data...X_train,X_test,y_train,y_test=load_data()...# create a simple neural network...classifier=...# for compatibility with the package the classifier should be constructed using tensorflow.keras...dbm=DBM(classifier)# create a DBM objectimg,img_confidence,_,_=dbm.generate_decision_boundary(X_train,y_train,X_test,y_test,resolution=256)# generate the decision boundarysdbm=SDBM(classifier)# create a SDBM objectimg,img_confidence,_,_=sdbm.generate_decision_boundary(X_train,y_train,X_test,y_test,resolution=256)# generate the decision boundary...# visualize the decision boundariesplt.imshow(img)plt.show()Created by Cristian Grosu for the master thesis project for 2023 at Utrecht University
Package: decision-python
DecipyMulti-Criteria Decision Making Methods libraryInstallation$pipinstalldecipyor$pipinstallgit+https://github.com/justsasri/decipy.git#egg=decipyMCDM Rankingimportnumpyasnpimportpandasaspdfromdecipyimportexecutorsasexe# define matrixmatrix=np.array([[4,3,2,4],[5,4,3,7],[6,5,5,3],])# alternativesalts=['A1','A2','A3']# criteriascrits=['C1','C2','C3','C4']# criteria's beneficial values, True for benefit or False for costbeneficial=[True,True,True,True]# criteria's weightsweights=[0.10,0.20,0.30,0.40]# define DataFramexij=pd.DataFrame(matrix,index=alts,columns=crits)# create Executor (MCDM Method implementation)kwargs={'data':xij,'beneficial':beneficial,'weights':weights,'rank_reverse':True,'rank_method':"ordinal"}# Build MCDM Executorwsm=exe.WSM(**kwargs)# Weighted Sum Methodtopsis=exe.Topsis(**kwargs)# Topsisvikor=exe.Vikor(**kwargs)# Vikor# show resultsprint("WSM Ranks")print(wsm.dataframe)print("TOPSIS Ranks")print(topsis.dataframe)print("Vikor Ranks")print(vikor.dataframe)# How to choose best MCDM Method ?# Instantiate Rank Analizeranalizer=exe.RankSimilarityAnalyzer()# Add MCDMs to anlizeranalizer.add_executor(wsm)analizer.add_executor(topsis)analizer.add_executor(vikor)# run analizerresults=analizer.analyze()print(results)referencesTriantaphyllou, E., Mann, S.H. 1989. "An Examination of The Effectiveness of Multi-dimensional Decision-making Methods: A Decision Making Paradox." Decision Support Systems (5(3)): 303–312.Chakraborty, S., and C.H. Yeh. 2012. "Rank Similarity based MADM Method Selection." International Conference on Statistics in Science, Business and Engineering (ICSSBE2012)Brauers, Willem K., and Edmundas K. Zavadskas. 2009. "Robustness of the multi‐objective MOORA method with a test for the facilities sector." Ukio Technologinisir Ekonominis (15:2): 352-375.Hwang, C.L., and K. Yoon. 1981. "Multiple attribute decision making, methods and applications." Lecture Notes in Economics and Mathematical Systems(Springer-Verlag) 186Yoon, K.P. and Hwang, C.L., “Multiple Attribute Decision Making: An Introduction”, SAGE publications, London, 1995.ÇELEN, Aydın. 2014. "Comparative Analysis of Normalization Procedures in TOPSIS Method: With an Application to Turkish Deposit Banking Market." INFORMATICA 25 (2): 185–208“Ranking”,http://en.wikipedia.org/wiki/Ranking
Package: decisionrulespy
DecisionRulesPyVERSION 3 CONTAINS MAJOR CHANGES IN API INTERFACES AND NOW SUPPORTS RULEFLOWS CRUDA simple library that allows you to easily connect toDecisionRules.iofrom your application.UsageSolver (DecisionTables, ScriptRules, RuleFlow)Management API (CRUD operations on DecisionTables and RuleFlows)1 - SolverSolver is designed for solving rules made in DecisionRules application.1.1 - Defining solver initBefore you start solving rules you need to setup an SolverApi instance. How to do so is shown below.importdecisionrulesasyncdefsolver_test():solver=decisionrules.SolverApi(solver_api_key)1.2 - Defining solver method with dataSolver method expects 5 argumentsSolverType - enum value - mandatoryRuleId - str - mandatoryData - dict or json str - mandatorySolverStrategy - enum value - mandatoryVersion - str - optionalasyncdefsolver_test():data={"say":"Hello from python"}solver=decisionrules.SolverApi(api_key)# SolverType enum defines type of solver (Rule or Compostion)response=awaitsolver.solve(decisionrules.SolverType.RULE,get_rule,data,decisionrules.SolverStrategies.STANDARD)response2=awaitsolver.solve(decisionrules.SolverType.RULEFLOW,compo_rule,data,decisionrules.SolverStrategies.STANDARD)1.3 Solver with custom domainFor using custom domain just addCustomDomaininstance to theinit methodwithurlandprotocolparameters.asyncdefsolver_test():data={"say":"Hello from python"}solver=decisionrules.SolverApi(api_key,decisionrules.CustomDomain("YOUR_URL",decisionrules.Protocols.HTTPS))response=awaitsolver.solve(decisionrules.SolverType.RULE,get_rule,data,decisionrules.SolverStrategies.STANDARD)response2=awaitsolver.solve(decisionrules.SolverType.RULEFLOW,compo_rule,data,decisionrules.SolverStrategies.STANDARD)2 - Management APIManagement api is accessible viaManagementApiand required management api key that you can obtain in api key section in DecisionRules app.Data for ruleflow import methods are represented as an array where index0 = RF, and index1...indexn are DTsasyncdefmanagement_api_test():manager=decisionrules.ManagementApi(mng_key)#Or with custom domainmanager=decisionrules.ManagementApi(mng_key,decisionrules.CustomDomain("YOUR_URL",decisionrules.Protocols.HTTPS))2.1 Management API usage exampleasyncdefmanagement_api_test():manager=decisionrules.ManagementApi(mng_key)awaitmanager.get_rule(get_rule)awaitmanager.get_rule(get_rule,"1")awaitmanager.get_space(get_space)awaitmanager.update_rule(put_rule,"1",put_data)awaitmanager.create_rule(post_rule,post_data)awaitmanager.delete_rule(delete_rule,"1")awaitmanager.create_ruleflow(new_ruleflow)awaitmanager.get_ruleflow(id)awaitmanager.get_ruleflow(id,1)awaitmanager.update_ruleflow(id,1,ruleflow)awaitmanager.export_ruleflow(id,1)awaitmanager.export_ruleflow(id)awaitmanager.import_ruleflow([new_ruleflow_with_dt],1,)awaitmanager.import_ruleflow(id,1,[new_ruleflow_with_dt])awaitmanager.import_ruleflow([new_ruleflow_with_dt])awaitmanager.delele_ruleflow(id)awaitmanager.getItems(tags)awaitmanager.updateTags(ruleId,data,version)awaitmanager.delete(ruleId,version)2.3 All available methods in management API
Package: decision-tool
Project descriptionDecision-tool is a simple yet effective decision analysis tool. Solves and visualizes Trees with recursive algorithm.UsageDecision trees are constructed usingTreeclass. it takes nested structure and returns a Tree instance. In the nested structure, dictinary represents decision, List represents discrete probability distribution and tuple represents possible outcome of a discrete probability distribution, scipy.stats can be used for continous probability distribution:example_tree = Tree({"option 1":25,
                     "option 2":[(.4,20,"result 1"), (.3,21,"result 2"), (.3,stats.norm(1,1),"result 3")]
                     })
example_tree.fig().show()Consider the example: You have 2 options as quit the job and not quit. if you quit, you will get a bonus of 8000. If you do not quit, you will promote to manager with a probability of 0.3, If you then quit, will get 16000, If you do not, you will get a random payoff distributed normally with mean 12000 and standart deviation of 1000. If you do not quit at the begining and do not promote, your PV will be normal random variable with mean 10000 and standart deviation of 3000.job_decision = Tree({
    "quit":8e3,
    "do not quit":[(0.3, 
                        {"quit":16e3,
                        "do not quit":stats.norm(12e3,1e3)
                        }
                    ,"promote"),

                    (0.7, stats.norm(10e3,3e3),"do not promote")
                    ]
})

job_decision.fig().show()The methodsolvereturns a Tree instance that only contains optimal solutions.solution = job_decision.solve()
print("optimal expected monetary value:" ,solution.mean,"variance:",solution.var)
solution.fig().show()optimal expected monetary value: 11800.0 variance: 13860000.0For indexing, order decision names and event names:print("Variance of not quiting and not getting a promote ",
      solution["do not quit","do not promote"].var)Variance of not quiting and not getting a promote  9000000.0Save and load decision trees usingsavemethod andload_treefunction.
Package: decision-tree-id3
decision-tree-id3decision-tree-id3 is a module created to derive decision trees using the
ID3 algorithm. It is written to be compatible with Scikit-learn’s API
using the guidelines for Scikit-learn-contrib. It is licensed under the
3-clause BSD license.Important LinksHTML Documentation -https://svaante.github.io/decision-tree-id3InstallationDependenciesPython (>= 2.7 or >= 3.3)NumPy (>= 1.6.1)Scikit-learn (>= 0.17)The package by itself comes with a single estimator Id3Estimator. To
install the module:pip install decision-tree-id3or clone the project using:git clone https://github.com/svaante/decision-tree-id3.git
cd decision-tree-id3
python setup.py installUsageIf the installation is successful, you should be able to execute the
following in Python:>>>fromsklearn.datasetsimportload_breast_cancer>>>fromid3importId3Estimator>>>fromid3importexport_graphviz>>>bunch=load_breast_cancer()>>>estimator=Id3Estimator()>>>estimator.fit(bunch.data,bunch.target)>>>export_graphviz(estimator.tree_,'tree.dot',bunch.feature_names)And to generate a PDF of the decision tree using GraphViz:dot-Tpdftree.dot-otree.pdfThere are a number of different default parameters to control the growth
of the tree: - max_depth, the max depth of the tree. -
min_samples_split, the minimum number of samples in a split to be
considered. - prune, if the tree should be post-pruned to avoid
overfitting and cut down on size. - gain_ratio, if the algorithm should
use gain ratio when splitting the data. - min_entropy_decrease, the
minimum decrease in entropy to consider a split. - is_repeating, repeat
the use of features.For more in depth information see the documentationhttps://svaante.github.io/decision-tree-id3
Package: decision-tree-ID3-Algorithm
Decision Tree algorithmsImplementation of Decision tree algorithmsInstallationRun the following command to install:pipinstalldecision-tree-ID3-AlgorithmUsagefromdecisiontree.ID3AlgorithmimportID3id3_2=ID3(dataset_train,headers_train,dataset_test,headers_test)# dataset_train contains the training dataset with headers as headers_train# dataset_test contains unlabled data with headers as headers_test# all the agruments are of type listid3_2.build_tree()id3_2.classify()
Package: decision-tree-id-fork
|Travis Status| |Coveralls Status| |CircleCI Status|decision-tree-id3-forkThis is a fork of decision-tree-id3. It is a module created to derive decision trees using the ID3 algorithm. It is written to be compatible with Scikit-learn's API using the guidelines for Scikit-learn-contrib. It is licensed under the 3-clause BSD license.Important LinksOriginal HTML Documentation can be found onhttps://svaante.github.io/decision-tree-id3InstallationDependencies-  Python (>= 2.7 or >= 3.3)
-  NumPy (>= 1.14.6)
-  Scikit-learn (>= 1.0)
-  Six (>= 0.9.0)

The package by itself comes with a single estimator Id3Estimator. To install the module:

::

    pip install decision-tree-id3-fork

or clone the project using:

::

    git clone https://github.com/DekwoKybon/decision-tree-id3.git
    cd decision-tree-id3
    python setup.py install

Usage
-----

If the installation is successful, you should be able to execute the
following in Python:

.. code:: python

    >>> from sklearn.datasets import load_breast_cancer
        >>> from src.id3 import Id3Estimator
        >>> from src.id3 import export_graphviz

        >>> bunch = load_breast_cancer()
        >>> estimator = Id3Estimator()
        >>> estimator = estimator.fit(bunch.data, bunch.target)
        >>> tree = export_graphviz(estimator.tree_, 'tree.dot', bunch.feature_names)


    And to generate a PDF of the decision tree using GraphViz:
    >>> from id3 import Id3Estimator
    >>> from id3 import export_graphviz

    >>> bunch = load_breast_cancer()
    >>> estimator = Id3Estimator()
    >>> estimator = estimator.fit(bunch.data, bunch.target)
    >>> tree = export_graphviz(estimator.tree_, 'tree.dot', bunch.feature_names)


And to generate a PDF of the decision tree using GraphViz:

.. code:: shell

    dot -Tpdf tree.dot -o tree.pdf

There are a number of different default parameters to control the growth
of the tree: - max\_depth, the max depth of the tree. -
min\_samples\_split, the minimum number of samples in a split to be
considered. - prune, if the tree should be post-pruned to avoid
overfitting and cut down on size. - gain\_ratio, if the algorithm should
use gain ratio when splitting the data. - min\_entropy\_decrease, the
minimum decrease in entropy to consider a split. - is\_repeating, repeat
the use of features.

For more in depth information see the documentation
https://svaante.github.io/decision-tree-id3

.. |Travis Status| image:: https://travis-ci.org/svaante/decision-tree-id3.svg?branch=master
   :target: https://travis-ci.org/svaante/decision-tree-id3
.. |Coveralls Status| image:: https://coveralls.io/repos/svaante/decision-tree-id3/badge.svg?branch=master
   :target: https://coveralls.io/r/svaante/decision-tree-id3
.. |CircleCI Status| image:: https://circleci.com/gh/svaante/decision-tree-id3.svg?style=shield&circle-token=:circle-token
   :target: https://circleci.com/gh/svaante/decision-tree-id3/tree/master
Package: decisiontree-lite
Decision Tree Implementation from ScratchOverviewThis package provides a Python implementation of a decision tree algorithm from scratch, along with training and evaluation utilities. This lightweight implementation has performance comparable to scikit-learn's implementation.FilesDecisionTree.py: Contains the core classes and functions for building and using the decision tree model.utils.py: Provides helper functions for loading datasets, splitting data, calculating evaluation metrics, and comparing custom and scikit-learn implementations.Key FeaturesCustom implementation of a decision tree algorithm.Supports both Gini impurity and entropy criteria for splitting.Options to control maximum depth and minimum samples for splitting.Functions for loading and processing various inbuilt datasets.Comparison with scikit-learn's DecisionTreeClassifier in terms of accuracy, precision, recall, F1-score, time taken, and memory usage.UsageMake sure the labels are numeric(Use label encoder).fromdecisiontree_lite.decisiontreeimportDecisionTreefromdecisiontree_lite.utilsimportload_data,train_test,model_metrics# You can use one of inbuilt datasets('iris', 'digits', 'breast_cancer', 'wine') or an external dataset of your choiceX,y=load_data('iris')# Perform test train splittest_split_size=0.3X_train,X_test,y_train,y_test=train_test(X,y,test_split_size)clf=DecisionTree()clf.fit(X_train,y_train)y_pred=clf.predict(X_test)accuracy,precision,recall,f1score=model_metrics(y_test,y_pred)print("Accuracy:{},\nPrecision:{},\nRecall:{},\nF1-score:{}".format(accuracy,precision,recall,f1score))CustomizationAdjust hyperparameters like max_depth, min_samples_split, and criteria in the DecisionTree to tune the model.max_depth: The maximum depth of the tree [1, 2, 4, 8, 16, 32, 64, 100].min_samples_split: The minimum number of samples required to split an internal node [2, 50, 100].criteria: To split based on gini or entropy impurity ['gini', 'entropy'].clf=DecisionTree(max_depth=maxDepth,min_samples_split=minSplit,criteria=impCriteria)Additional NotesThe code is structured for clarity and modularity.Comments are included to explain key concepts and steps.Feel free to contribute to this project or use it for your own learning and experimentation!
Package: decision-tree-morfist
morfist: mixed-output-rfMulti-target Random Forest implementation that can mix both classification and regression tasks.Morfist implements the Random Forest algorithm (Breiman, 2001)
with support for mixed-task multi-task learning, i.e., it is possible to train the model on any number
of classification tasks and regression tasks, simultaneously. Morfist's mixed multi-task learning implementation follows that proposed by Linusson (2013).Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.Linusson, H. (2013). Multi-output random forests.InstallationWith pip:pip install decision-tree-morfistWith conda:conda install -c systemallica decision-tree-morfistUsageInitialising the modelSimilarly to a scikit-learnRandomForestClassifier, a MixedRandomForest can be initialised in this way:from morfist import MixedRandomForest

mrf = MixedRandomForest(
    n_estimators=n_trees,
    min_samples_leaf=1,
    classification_targets=[0]
)For more info on the possible parameters, visit thedocumentation.Training the modelOnce the model is initialised, it can be fitted like this:mrf.fit(X, y)Where X are the training examples and Y are their respective labels(if they are categorical) or values(if they are numerical)PredictionThe model can be now used to predict new instances.Class/value:mrf.predict(x)Probability:mrf.predict_proba(x)TODO:Speed up the learning algorithm implementation (morfist is currentlymuchslower than the Random Forest implementation available in scikit-learn)