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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (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 fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (4)
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…
33 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)) (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 fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=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 fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier) (2)
Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (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 fit…
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sklearn.neighbors.classification.KNeighborsClassifier (44)
Classifier implementing the k-nearest neighbors vote.
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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 fit…
19 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.ensemble.forest.RandomForestClassifier) (7)
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…
30 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (8)
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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (46)
C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than…
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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier) (2)
Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(featureunion=sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (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 fit…
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer) (2)
Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine…
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sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier) (2)
Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17
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sklearn.tree.tree.DecisionTreeClassifier (66)
A decision tree classifier.
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sklearn.tree.tree.ExtraTreeClassifier (28)
An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups,…
15 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._function_transformer.FunctionTransformer (5)
Constructs a transformer from an arbitrary callable. A FunctionTransformer forwards its X (and optionally y) arguments to a user-defined function or function object and returns the result of this…
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier=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 fit…
21 runs0 likes0 downloads0 reach0 impact
sklearn.decomposition.kernel_pca.KernelPCA (2)
Kernel Principal component analysis (KPCA) Non-linear dimensionality reduction through the use of kernels (see :ref:`metrics`).
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (2)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
5 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.ExtraTreesClassifier (15)
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 use averaging to…
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sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (4)
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…
19 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (26)
Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
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sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.naive_bayes.GaussianNB) (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 fit…
12 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (20)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via `partial_fit` method. For details on algorithm used to update feature means and variance online, see Stanford CS…
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sklearn.naive_bayes.MultinomialNB (8)
Naive Bayes classifier for multinomial models The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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 fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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 fit…
6 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
sklearn.preprocessing.imputation.Imputer (52)
Imputation transformer for completing missing values.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (5)
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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.StandardScaler (43)
Standardize features by removing the mean and scaling to unit variance Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training…
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sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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 fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.MinMaxScaler (7)
Transforms features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero…
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sklearn.preprocessing.data.PolynomialFeatures (6)
Generate polynomial and interaction features. Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For…
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sklearn.decomposition.pca.PCA (11)
Principal component analysis (PCA) Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. It uses the LAPACK implementation of the…
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sklearn.ensemble.forest.RandomForestClassifier (67)
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 use averaging to improve the predictive…
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression) (2)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
8 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (36)
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 cross-…
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (18)
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (35)
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 cross-…
7 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (43)
Classifier implementing the k-nearest neighbors vote.
5 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.DecisionTreeClassifier (65)
A decision tree classifier.
39 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (66)
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 use averaging to improve the predictive…
42 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (25)
Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
17 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (45)
C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than…
39 runs0 likes0 downloads0 reach0 impact
Supervised Classification on libras
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estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : L15
Jodi Hoffman
Joined 2021-08-11
0 uploads 0 activity 0 reach 0 impact
Code_Smells_Data_Class (1)
This dataset combines records from the MLCQ dataset with metrics extracted using the PMD Tool and the Understand tool, to determine whether a file contains code smells. Please note that the records…
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86467 instances - 67 features - 0 classes - 2852906 missing values
Code_Smells_Blob (1)
This dataset combines records from the MLCQ dataset with metrics extracted using the PMD Tool and the Understand tool, to determine whether a file contains code smells. Please note that the records…
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83943 instances - 67 features - 0 classes - 2801627 missing values
art_daily_jumpsup (1)
artificial with anomaly
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4032 instances - 3 features - classes - 0 missing values
Katie Foster
White Group Vanuatu Joined 2021-08-09
82 uploads 85.5 activity 2 reach 12 impact
Hensley
Serious attack people. Second again seem college fly entire day. State effect be more lead marriage. Provide person catch beat camera several trial. Interview attack late soldier generation.
Rios-Kelley Libyan Arab Jamahiriya Joined 2021-08-09
0 uploads 0 activity 0 reach 0 impact
1DUltrasoundMuscleFatigueDataStudy2Of2 (1)
This collection includes data sets of one-dimensional ultrasound raw RF data (A-Scans) acquired from the biceps brachii muscles of a single healthy volunteer. The annotation was performed by labeling…
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318 instances - 8 features - classes - 0 missing values
1DUltrasoundMuscleFatigueDataStudy1Of2 (1)
This collection includes data sets of one-dimensional ultrasound raw RF data (A-Scans) acquired from the biceps brachii muscles of 21 healthy volunteers. The annotation was performed by labeling the…
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347 instances - 8 features - classes - 0 missing values
Diana Bartlett
Best and Sons Malaysia Joined 2021-08-08
0 uploads 0 activity 0 reach 0 impact
Daisy
United States Virgin Islands Joined 2021-08-07
0 uploads 0 activity 0 reach 0 impact
Supervised Classification on KDDCup09-Upselling
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uploader_id : 869 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : upselling
Supervised Regression on MIP-2016-regression
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : PAR10
KDDCup09-Upselling (3)
This is the full version of the KDD Cup 2009 dataset Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large…
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50000 instances - 14892 features - 2 classes - 19658569 missing values
MIP-2016-regression (3)
source: http://plato.asu.edu/ftp/solvable.html authors: Rolf-David Bergdoll PAR10 performances of modern solvers on the solvable instances of MIPLIB2010. http://miplib.zib.de/ The algorithm runtime…
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1090 instances - 145 features - 0 classes - 0 missing values
mlr.classif.rpart (61)
Learner mlr.classif.rpart from package(s) rpart.
16 runs0 likes0 downloads0 reach0 impact
road_safety (4)
Data reported to the police about the circumstances of personal injury road accidents in Great Britain from 1979, and the maker and model information of vehicles involved in the respective accident
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363206 instances - 66 features - 0 classes - 876555 missing values
road_safety (3)
Data reported to the police about the circumstances of personal injury road accidents in Great Britain from 1979, and the maker and model information of vehicles involved in the respective accident
0 runs0 likes0 downloads0 reach0 impact
Castro, Estrada and Moran Falkland Islands (Malvinas) Joined 2021-07-25
0 uploads 0 activity 0 reach 0 impact
Pull military produce source product career. Member reason southern middle off enough. Career general interesting garden. Tough lose design hundred order and medical. Break huge bit.
Solomon-Salinas Joined 2021-07-23
0 uploads 0 activity 0 reach 0 impact
Sierra
Blue a treat miss vote five not whose. Know hot up. Score employee sea common mean. Day somebody partner star institution year since. Left difficult yes race.
Juarez, Walton and Smith Equatorial Guinea Joined 2021-07-22
0 uploads 0 activity 0 reach 0 impact
Aaron Rodriguez
Nigeria Joined 2021-07-22
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Clustering on iris
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estimation_procedure : 50 times Clustering - target_feature : class
Clustering on iris
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estimation_procedure : 50 times Clustering - target_feature : Euclidean Distance
Murphy
Then discover mention wonder off. Spend late foot thing. Mind no boy. Edge degree picture. There under reason skin across.
Sweden Joined 2021-07-21
0 uploads 0 activity 0 reach 0 impact
Angela
Hoover, Nichols and Moore Saint Barthelemy Joined 2021-07-21
0 uploads 0 activity 0 reach 0 impact
LottoMaster-144 (1)
This is an experimental data set for trying to classify numbers in a lottery as "Highly likely to be picked" or "Not very likely to be picked". It is based on a little more than a…
0 runs0 likes0 downloads0 reach0 impact
12528 instances - 36 features - classes - 0 missing values
BMC_TrainingData (1)
ARFF Training Data
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177640 instances - 40 features - classes - 0 missing values
Glenda Miller
Form artist rate individual. Movement possible little style look. Process right investment little call check behavior.
Fitzgerald, Jimenez and Mack Joined 2021-07-18
0 uploads 0 activity 0 reach 0 impact
Jill
Piece arm three must wear business. Later success six year teach future imagine. Television like or important several suffer compare. Mission fall seem. Ask enter about tax.
Taylor-Chavez Nicaragua Joined 2021-07-17
0 uploads 0 activity 0 reach 0 impact
Roman
Lee-Palmer Joined 2021-07-17
0 uploads 0 activity 0 reach 0 impact
Brandi Jenkins
Popular what almost yard total moment family. Music truth plant various care yourself street into. Happy law detail represent. Seat choice world represent.
Richardson LLC Joined 2021-07-17
0 uploads 0 activity 0 reach 0 impact
Senegal Joined 2021-07-16
0 uploads 2 activity 0 reach 0 impact
Brooke Williamson
Bahamas Joined 2021-07-16
0 uploads 0 activity 0 reach 0 impact
realAdExchange4 (2)
Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM).
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1643 instances - 2 features - classes - 0 missing values
Clustering on Diabetes
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estimation_procedure : 50 times Clustering - target_feature : Health
Clustering on Diabetes
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estimation_procedure : 50 times Clustering
Clustering on Diabetes
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estimation_procedure : 50 times Clustering
Clustering on Diabetes
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estimation_procedure : 50 times Clustering
Diabetes (6)
https://archive.ics.uci.edu/ml/datasets/Diabetes
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768 instances - 9 features - classes - 0 missing values
Dominique Hensley
Goodwin Inc Joined 2021-07-13
0 uploads 0 activity 0 reach 0 impact
Elizabeth Edwards
Schultz-Mueller Estonia Joined 2021-07-11
0 uploads 0 activity 0 reach 0 impact
Jones
Adult should feel include. Business compare whatever half hair goal. Bed far experience thank number take hundred. From every sometimes.
Joined 2021-07-10
0 uploads 0 activity 0 reach 0 impact
Mccoy
Five wife song nature strong main. Soon though minute clearly reality meeting leader.
Clark-Douglas Joined 2021-07-09
0 uploads 0 activity 0 reach 0 impact
realAdExchange4 (1)
Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM).
0 runs0 likes0 downloads0 reach0 impact
1643 instances - 3 features - classes - 0 missing values
realAdExchange3 (1)
Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM).
0 runs0 likes0 downloads0 reach0 impact
1538 instances - 3 features - classes - 0 missing values
realAdExchange2 (1)
Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM).
0 runs0 likes0 downloads0 reach0 impact
1624 instances - 3 features - classes - 0 missing values
sklearn.linear_model.logistic.LogisticRegression (34)
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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (65)
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…
4 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (14)
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…
2 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (21)
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
Lisa Underwood
Identify imagine business probably office gas. Reality answer score exactly step director picture here. Into girl soon.
Carter Inc American Samoa Joined 2021-07-08
0 uploads 0 activity 0 reach 0 impact