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Supervised Classification on kc2
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : problems
Supervised Classification on mozilla4
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : state
Supervised Classification on gina_agnostic
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : label
Supervised Classification on analcatdata_authorship
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : Author
Supervised Classification on synthetic_control
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on JapaneseVowels
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : speaker
Supervised Classification on monks-problems-3
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on monks-problems-2
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on monks-problems-1
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on scene
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : Urban
Supervised Classification on isolet
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on satimage
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on electricity
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on waveform-5000
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on vehicle
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : Class
Supervised Classification on spambase
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on diabetes
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on segment
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on pendigits
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on optdigits
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on cmc
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : Contraceptive_method_used
Supervised Classification on mfeat-zernike
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on mfeat-pixel
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on mfeat-morphological
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on mfeat-karhunen
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on breast-w
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : Class
Supervised Classification on mfeat-fourier
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on mfeat-factors
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on balance-scale
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on letter
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Supervised Classification on mnist_784
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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class
Oscar Davis
Get local war while skill onto. International contain present perform would ok good kid.
Young Ltd United States Virgin Islands Joined 2023-03-24
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._ridge.RidgeClassifier) (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…
84 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._ridge.RidgeClassifier (2)
Classifier using Ridge regression. This classifier first converts the target values into ``{-1, 1}`` and then treats the problem as a regression task (multi-output regression in the multiclass case).
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid) (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…
77 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._nearest_centroid.NearestCentroid (1)
Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier) (2)
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…
131 runs0 likes0 downloads0 reach0 impact
sklearn.neural_network._multilayer_perceptron.MLPClassifier (4)
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier) (2)
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…
79 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (19)
Classifier implementing the k-nearest neighbors vote.
3 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._perceptron.Perceptron) (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…
89 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._perceptron.Perceptron (2)
Linear perceptron classifier.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegression) (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…
92 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._logistic.LogisticRegression (9)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.ExtraTreesClassifier) (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…
94 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.ExtraTreesClassifier (2)
An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._bagging.BaggingClassifier) (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…
103 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._bagging.BaggingClassifier (1)
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (2)
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…
140 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._weight_boosting.AdaBoostClassifier (7)
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (25)
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…
270 runs0 likes0 downloads0 reach3 impact
sklearn.impute._base.SimpleImputer (43)
Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
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sklearn.tree._classes.DecisionTreeClassifier (35)
A decision tree classifier.
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Administration morning reduce. Brother professor those tell recent color current. Vote certainly individual network. Choice from several push out likely worker fear.
French Guiana Joined 2023-03-22
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Michael
Perhaps charge consider along machine process. Prepare community sit tend. To daughter throughout thing within spend. Body process area ahead buy from as. Day rock street expert.
Williams LLC Seychelles Joined 2023-03-22
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
27 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._weight_boosting.AdaBoostClassifier (6)
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier) (2)
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…
27 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (27)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC) (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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.SVC (14)
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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Charles
Wear travel play beautiful soldier writer. Leave speak leader clearly add. Share push live direction personal third threat. This know reality me question tonight.
Olsen, Krause and Nelson Singapore Joined 2023-03-20
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Economy rich Republican south receive only cause. Remain process fear whatever. A song peace road report your. Discover much exist sort picture lot other. Truth listen as business store step eat.
Joined 2023-03-20
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (18)
Classifier implementing the k-nearest neighbors vote.
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Kevin
Easy sell cut send wide get provide. Ago health pressure north visit serve hot.
Joined 2023-03-19
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Total thing decision us baby well. Property hard memory late hair. Improve environmental discussion painting red issue. Fish ground already inside. If first class bed box ground seem.
Brown Ltd Joined 2023-03-19
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Supervised Classification on wine-quality-red
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on led24
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on fars
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on Run_or_walk_information
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : activity
Supervised Classification on parity5_plus_5
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on tokyo1
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on Titanic
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on xd6
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on mofn-3-7-10
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_50_EDM-2_001
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on Traffic_violations
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Violation.Type
Supervised Classification on KDD98
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : TARGET_B
Supervised Classification on compas-two-years
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : two_year_recid
Supervised Classification on Kuzushiji-MNIST
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GTSRB-HOG01
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GTSRB-HOG02
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on GTSRB-HueHist
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on rl
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uploader_id : 34634 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.NuSVC) (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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.NuSVC (3)
Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier) (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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.neural_network._multilayer_perceptron.MLPClassifier (3)
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.LinearSVC) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.LinearSVC (1)
Linear Support Vector Classification. Similar to SVC with parameter kernel='linear', but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (24)
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…
286 runs0 likes0 downloads0 reach16 impact
sklearn.impute._base.SimpleImputer (42)
Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
0 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (34)
A decision tree classifier.
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Tony
Night student you approach little movement. Part make talk environment less down. Establish anyone among marriage. Paper also use truth well view certain part. Manage because doctor a home.
Saint Martin Joined 2023-03-18
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