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mlr.classif.earth.preproc (1)
Learner mlr.classif.earth.preproc from package(s) !earth.
57 runs0 likes0 downloads0 reach0 impact
mlr.classif.featureless.preproc (2)
Learner mlr.classif.featureless.preproc from package(s) mlr.
57 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn.preproc (7)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
57 runs0 likes0 downloads0 reach0 impact
mlr.classif.naiveBayes.preproc (10)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
57 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
57 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
57 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
57 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
57 runs0 likes0 downloads0 reach0 impact
weka.LWL_DecisionStump (1)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
56 runs0 likes1 downloads1 reach0 impact
weka.NaiveBayesMultinomialUpdateable (2)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
56 runs0 likes4 downloads4 reach48 impact
weka.RealAdaBoost_IBk (1)
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
56 runs0 likes2 downloads2 reach51 impact
mlr.classif.ctree.preproc (2)
Learner mlr.classif.ctree.preproc from package(s) party.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.gbm.preproc (4)
Learner mlr.classif.gbm.preproc from package(s) gbm.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet.preproc (3)
Learner mlr.classif.glmnet.preproc from package(s) glmnet.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.ksvm.preproc (3)
Learner mlr.classif.ksvm.preproc from package(s) kernlab.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForestSRC.preproc (2)
Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc (20)
Learner mlr.classif.rpart.preproc from package(s) rpart.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.rda.preproc (2)
Learner mlr.classif.rda.preproc from package(s) klaR.
56 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm.preproc (4)
Learner mlr.classif.svm.preproc from package(s) e1071.
56 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation) (5)
Automatically created scikit-learn flow.
56 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,AveragePooling2D,Flatten,Dense) (2)
Automatically created scikit-learn flow.
56 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree) (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
56 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
56 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Discretize_J48 (1)
Weka implementation of FilteredClassifier
55 runs0 likes1 downloads1 reach0 impact
weka.MultiClassClassifierUpdateable_SGD (1)
Weka implementation of MultiClassClassifierUpdateable
55 runs0 likes1 downloads1 reach0 impact
weka.MultiScheme_ZeroR (1)
Weka implementation of MultiScheme
55 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (8)
Flow generated by openml_run
55 runs0 likes5 downloads5 reach17 impact
moa.NaiveBayesMultinomial (1)
Moa implementation of NaiveBayesMultinomial
55 runs0 likes2 downloads2 reach47 impact
moa.StackingAttemptV2_Perceptron (1)
Moa implementation of StackingAttemptV2
55 runs0 likes2 downloads2 reach46 impact
mlr.classif.cvglmnet.preproc (2)
Learner mlr.classif.cvglmnet.preproc from package(s) glmnet.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.evtree.preproc (2)
Learner mlr.classif.evtree.preproc from package(s) evtree.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.gausspr.preproc (2)
Learner mlr.classif.gausspr.preproc from package(s) kernlab.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.mda.preproc (1)
Learner mlr.classif.mda.preproc from package(s) !mda.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.nnet.preproc (4)
Learner mlr.classif.nnet.preproc from package(s) nnet.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest.preproc (5)
Learner mlr.classif.randomForest.preproc from package(s) randomForest.
55 runs0 likes0 downloads0 reach0 impact
mlr.classif.RRF.preproc (2)
Learner mlr.classif.RRF.preproc from package(s) RRF.
55 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (31)
Automatically created scikit-learn flow.
55 runs0 likes0 downloads0 reach53 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
55 runs0 likes0 downloads0 reach0 impact
weka.RandomCommittee_RandomTree (1)
Weka implementation of RandomCommittee
54 runs0 likes1 downloads1 reach0 impact
weka.DecisionTable_BestFirst (1)
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
54 runs0 likes1 downloads1 reach0 impact
moa.StackingAttempt_SGD (1)
Moa implementation of StackingAttempt
54 runs0 likes2 downloads2 reach46 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
54 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_M5P (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
53 runs0 likes1 downloads1 reach0 impact
moa.StackingAttempt_SPegasos (1)
Moa implementation of StackingAttempt
53 runs0 likes2 downloads2 reach44 impact
moa.StackingAttemptV2_SPegasos (1)
Moa implementation of StackingAttemptV2
53 runs0 likes2 downloads2 reach45 impact
weka.CostSensitiveClassifier_Bagging_RandomTree (3)
Weka implementation of CostSensitiveClassifier
53 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (17)
Learner mlr.classif.glmnet from package(s) glmnet.
53 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
53 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_ZeroR (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
52 runs0 likes1 downloads1 reach0 impact
weka.RandomSubSpace_REPTree (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
52 runs0 likes1 downloads1 reach0 impact
weka.Vote_ZeroR (1)
Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
52 runs0 likes1 downloads1 reach0 impact
moa.StackingAttemptV2_NaiveBayes (1)
Moa implementation of StackingAttemptV2
52 runs0 likes2 downloads2 reach33 impact
classif.IBk (9)
Learner classif.IBk from package(s) RWeka.
52 runs0 likes1 downloads1 reach0 impact
__main__.AUCLGBMClassifier (1)
Automatically created scikit-learn flow.
52 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dropout,Dense,Dropout,Dense) (1)
Automatically created scikit-learn flow.
52 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (6)
Automatically created scikit-learn flow.
52 runs0 likes0 downloads0 reach52 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
52 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifier_Logistic (1)
Weka implementation of MultiClassClassifier
51 runs0 likes1 downloads1 reach0 impact
weka.Logistic (2)
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
51 runs0 likes1 downloads1 reach0 impact
weka.MultiBoostAB_J48 (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
51 runs0 likes2 downloads2 reach46 impact
weka.MultiBoostAB_REPTree (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
51 runs0 likes2 downloads2 reach46 impact
moa.StackingAttemptV2_HoeffdingTree (1)
Moa implementation of StackingAttemptV2
51 runs0 likes2 downloads2 reach11 impact
classif.nnTrain (6)
Learner classif.nnTrain from package(s) deepnet.
51 runs0 likes1 downloads1 reach0 impact
flows.TpotFlow.TpotFlow (1)
Flow generated by run_task
51 runs0 likes0 downloads0 reach0 impact
mlr.classif.lda.preproc (2)
Learner mlr.classif.lda.preproc from package(s) MASS.
51 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_BayesNet (2)
Weka implementation of AttributeSelectedClassifier
51 runs0 likes0 downloads0 reach50 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
51 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
51 runs0 likes0 downloads0 reach0 impact
weka.KStar (2)
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
50 runs0 likes2 downloads2 reach37 impact
weka.NBTree (2)
Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
50 runs0 likes2 downloads2 reach45 impact
classif.randomForest (1)
Learner classif.randomForest from package(s) randomForest.
50 runs0 likes2 downloads2 reach5 impact
weka.A2DEUpdateable (1)
Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
50 runs0 likes2 downloads2 reach1 impact
weka.FilteredClassifier_CostSensitiveClassifier_Bagging_RandomTree (1)
Weka implementation of FilteredClassifier
50 runs0 likes0 downloads0 reach49 impact
mlr.classif.cforest.preproc (3)
Learner mlr.classif.cforest.preproc from package(s) party.
50 runs0 likes0 downloads0 reach0 impact
mlr.classif.multinom.preproc (2)
Learner mlr.classif.multinom.preproc from package(s) nnet.
50 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
50 runs0 likes0 downloads0 reach0 impact
sklearn.dummy.DummyClassifier (3)
Automatically created scikit-learn flow.
50 runs0 likes0 downloads0 reach33 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
50 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
50 runs0 likes0 downloads0 reach0 impact
weka.HoeffdingTree (1)
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
49 runs0 likes3 downloads3 reach37 impact
moa.LeveragingBag_Perceptron (1)
Moa implementation of LeveragingBag
49 runs0 likes1 downloads1 reach0 impact
classif.ranger (8)
Learner classif.ranger from package(s) ranger.
49 runs0 likes1 downloads1 reach0 impact
mlr.classif.rpart.preproc (5)
Learner mlr.classif.rpart.preproc from package(s) rpart.
49 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.tree.tree.DecisionTreeClassifier) (3)
Automatically created scikit-learn flow.
49 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
49 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
49 runs0 likes0 downloads0 reach0 impact
classif.rpart (49)
Learner classif.rpart from package(s) rpart.
48 runs0 likes1 downloads1 reach0 impact
__main__.PythonChallengeClassifier (4)
Automatically created scikit-learn flow.
48 runs0 likes0 downloads0 reach40 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
48 runs0 likes0 downloads0 reach46 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
48 runs0 likes0 downloads0 reach0 impact
weka.ADTree (2)
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
47 runs0 likes2 downloads2 reach44 impact
weka.NaiveBayesMultinomial (2)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
47 runs0 likes7 downloads7 reach46 impact
weka.LWL_RandomForest (2)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
47 runs0 likes2 downloads2 reach32 impact
moa.AccuracyWeightedEnsemble_WEKAClassifier_VotedPerceptron (1)
Moa implementation of AccuracyWeightedEnsemble
47 runs0 likes1 downloads1 reach0 impact
weka.MultiBoostAB_IBk (3)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
47 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_RandomTree (1)
Weka implementation of FilteredClassifier
47 runs0 likes0 downloads0 reach45 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
47 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
47 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.naive_bayes.MultinomialNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
47 runs0 likes0 downloads0 reach0 impact
weka.M5P (2)
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…
46 runs0 likes1 downloads1 reach0 impact