Showing 30 of 1290 results (0.002 seconds)
molecular-biology_promoters
1. Title of Database: E. coli promoter gene sequences (DNA) with associated imperfect domain theory 2. Sources: (a)...
6265 runs
anneal
1. Title of Database: Annealing Data 2. Source Information: donated by David Sterling and Wray Buntine. 3. Past Usage: unknown 4....
5782 runs
spect_train
1. Title of Database: SPECT heart data 2. Sources: -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan University of Colorado at...
5532 runs
spect_test
1. Title of Database: SPECT heart data 2. Sources: -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan University of Colorado at...
5527 runs
monks-problems-2_test
1. Title: The Monk's Problems 2. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon...
5514 runs
anneal.ORIG
1. Title of Database: Annealing Data 2. Source Information: donated by David Sterling and Wray Buntine. 3. Past Usage: unknown 4. Relevant...
5493 runs
monks-problems-1_train
1. Title: The Monk's Problems 2. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon...
5473 runs
breast-cancer
Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. ...
5454 runs
monks-problems-3_test
1. Title: The Monk's Problems 2. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon...
5444 runs
monks-problems-1_test
1. Title: The Monk's Problems 2. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon...
5428 runs
breast-w
5422 runs
monks-problems-2_train
1. Title: The Monk's Problems 2. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon...
5396 runs
vote
1. Title: 1984 United States Congressional Voting Records Database 2. Source Information: (a) Source: Congressional Quarterly Almanac, 98th...
5390 runs
shuttle-landing-control
1. Title: Space Shuttle Autolanding Domain 2. Sources: (a) Original source: unknown -- NASA: Mr. Roger Burke's autolander design...
5369 runs
kr-vs-kp
1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7 (usually abbreviated KRKPA7). The pawn on a7 means it is one square away from...
5364 runs
tic-tac-toe
1. Title: Tic-Tac-Toe Endgame database 2. Source Information -- Creator: David W. Aha (aha@cs.jhu.edu) -- Donor: David W. Aha...
5357 runs
iris
1. Title: Iris Plants Database 2. Sources: (a) Creator: R.A. Fisher (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) ...
5348 runs
car
1. Title: Car Evaluation Database 2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) ...
5324 runs
diabetes
1. Title: Pima Indians Diabetes Database 2. Sources: (a) Original owners: National Institute of Diabetes and Digestive and ...
5308 runs
haberman
1. Title: Haberman's Survival Data 2. Sources: (a) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (b) Date: March 4, 1999 3. Past...
5303 runs
credit-g
Description of the German credit dataset. 1. Title: German Credit data 2. Source Information Professor Dr. Hans Hofmann Institut f"ur...
5294 runs
sonar
NAME: Sonar, Mines vs. Rocks SUMMARY: This is the data set used by Gorman and Sejnowski in their study of the classification of sonar signals...
5292 runs
mushroom
1. Title: Mushroom Database 2. Sources: (a) Mushroom records drawn from The Audubon Society Field Guide to North American...
5282 runs
trains
1. Title: INDUCE Trains Data set 2. Sources: - Donor: GMU, Center for AI, Software Librarian, Eric E. Bloedorn...
5282 runs
liver-disorders
1. Title: BUPA liver disorders 2. Source information: -- Creators: BUPA Medical Research Ltd. -- Donor: Richard S. Forsyth ...
5281 runs
colic
1. Title: Horse Colic database 2. Source Information -- Creators: Mary McLeish & Matt Cecile Department of Computer Science ...
5255 runs
heart-statlog
This database contains 13 attributes (which have been extracted from a larger set of 75) Attribute Information: ...
5253 runs
spectf_test
1. Title of Database: SPECTF heart data 2. Sources: -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan University of Colorado at...
5249 runs
credit-a
1. Title: Credit Approval 2. Sources: (confidential) Submitted by quinlan@cs.su.oz.au 3. Past Usage: See Quinlan, *...
5244 runs
colic.ORIG
1. Title: Horse Colic database 2. Source Information -- Creators: Mary McLeish & Matt Cecile Department of Computer Science ...
5230 runs
Showing 30 of 326 results (0.002 seconds)
weka.Evaluation(1.86)
Class for evaluating machine learning models. ------------------------------------------------------------------- General options when evaluating...
107829 runs
weka.Bagging(1.31.2.2)
Implementation for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more information,...
90703 runs
weka.J48(1.2)
Implementation for generating a pruned or unpruned C4.5 decision tree. For more information, see Ross Quinlan (1993). "C4.5: Programs for Machine...
79946 runs
weka.SMO(1.53.2.2)
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces...
77559 runs
weka.MultiBoostAB(1.6.2.2)
Implementation for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique...
59049 runs
weka.AdaBoostM1(1.24.2.3)
Implementation for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically...
57563 runs
weka.MultilayerPerceptron(1.2)
This neural network uses backpropagation to train.
32123 runs
weka.LogitBoost(1.33)
Implementation for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can...
15955 runs
weka.RandomForest(1.6)
Implementation for constructing a forest of random trees. For more information see: Leo Breiman. "Random Forests". Machine Learning 45 (1):5-32,...
13946 runs
weka.OneR(1.17)
Implementation for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric...
9092 runs
weka.REPTree(1.19.2.2)
Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with...
5224 runs
weka.PART(1.2.2.1)
Implementation for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and makes the...
5190 runs
weka.RandomTree(1.8.2.2)
Implementation for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning.
5175 runs
weka.JRip(1.14)
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William...
5131 runs
weka.ConjunctiveRule(1.10)
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents...
5112 runs
weka.RBFNetwork(1.4)
Implementation that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the...
5012 runs
weka.DecisionStump(1.18)
Implementation for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared...
3849 runs
weka.ZeroR(1.11)
Implementation for building and using a 0-R classifier. Predicts the mean (for a numeric class) or the mode (for a nominal class).
3561 runs
weka.NaiveBayes(1.16)
The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables. Version: ...
3535 runs
weka.NBTree(1.3.2.1)
Implementation for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi (1996). Scaling up...
3501 runs
weka.Ridor(1.12)
The implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least...
3501 runs
weka.HyperPipes(1.15)
Implementation implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category...
3491 runs
weka.Logistic(1.32)
Implementation for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however,...
3480 runs
weka.LWL(1.12)
Implementation for performing locally weighted learning. Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression)....
3473 runs
weka.SimpleLogistic(1.5.2.1)
Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the...
3453 runs
weka.IBk(1.32)
K-nearest neighbours classifier. Normalizes attributes by default. Can select appropriate value of K based on cross-validation. Can also do distance...
3448 runs
weka.LMT(1.2.2.1)
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. The algorithm can...
3440 runs
weka.NNge(1.2.2.1)
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). For more...
3434 runs
weka.IB1(1.13.2.1)
Nearest-neighbour classifier. Uses normalized Euclidean distance to find the training instance closest to the given test instance, and predicts the...
3429 runs
weka.NaiveBayesUpdateable(1.4)
Implementation for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.This classifier will use a default...
3406 runs
Showing 49 of 49 results (0.002 seconds)
area_under_roc_curve
The area under the ROC curve (AUROC), calculated using the Mann-Whitney U-test. The curve is constructed by shifting the threshold for a positive...
build_cpu_time
The time in seconds to build a single model on all data.
build_memory
The memory, in bytes, needed to build a single model on all data.
class_complexity
Entropy, in bits, of the class distribution generated by the model's predictions. Calculated by taking the sum of -log2(predictedProb) over all...
class_complexity_gain
Entropy reduction, in bits, between the class distribution generated by the model's predictions, and the prior class distribution. Calculated by...
confusion_matrix
The confusion matrix, or contingency table, is a table that summarizes the number of instances that were predicted to belong to a certain class,...
correlation_coefficient
The sample Pearson correlation coefficient, or 'r': r = \frac{\sum ^n _{i=1}(X_i - \bar{X})(Y_i - \bar{Y})}{\sqrt{\sum ^n _{i=1}(X_i -...
f_measure
The F-Measure is the harmonic mean of precision and recall, also known as the the traditional F-measure, balanced F-score, or...
kappa
Cohen's kappa coefficient is a statistical measure of agreement for qualitative (categorical) items: it measures the agreement of prediction with the...
kb_relative_information_score
The Kononenko and Bratko Information score, divided by the prior entropy of the class distribution. See: Kononenko, I., Bratko, I.:...
kohavi_wolpert_bias_squared
Bias component (squared) of the bias-variance decomposition as defined by Kohavi and Wolpert in: R. Kohavi & D. Wolpert (1996), Bias plus variance...
kohavi_wolpert_error
Error rate measured in the bias-variance decomposition as defined by Kohavi and Wolpert in: R. Kohavi & D. Wolpert (1996), Bias plus variance...
kohavi_wolpert_sigma_squared
Intrinsic error component (squared) of the bias-variance decomposition as defined by Kohavi and Wolpert in: R. Kohavi and D. Wolpert (1996), Bias...
kohavi_wolpert_variance
Variance component of the bias-variance decomposition as defined by Kohavi and Wolpert in: R. Kohavi and D. Wolpert (1996), Bias plus variance...
kononenko_bratko_information_score
Kononenko and Bratko Information score. This measures predictive accuracy but eliminates the influence of prior probabilities. See: Kononenko,...
matthews_correlation_coefficient
The Matthews correlation coefficient takes into account true and false positives and negatives and is generally regarded as a balanced measure which...
mean_absolute_error
The mean absolute error (MAE) measures how close the model's predictions are to the actual target values. It is the sum of the absolute value of the...
mean_class_complexity
The entropy of the class distribution generated by the model (see class_complexity), divided by the number of instances in the input data.
mean_class_complexity_gain
The entropy gain of the class distribution by the model over the prior distribution (see class_complexity_gain), divided by the number of instances...
mean_f_measure
Unweighted(!) macro-average F-Measure. In macro-averaging, F-measure is computed locally over each category first and then the average over all...
mean_kononenko_bratko_information_score
Kononenko and Bratko Information score, see kononenko_bratko_information_score, divided by the number of instances in the input...
mean_precision
Unweighted(!) macro-average Precision. In macro-averaging, Precision is computed locally over each category first and then the average over all...
mean_prior_absolute_error
The mean prior absolute error (MPAE) is the mean absolute error (see mean_absolute_error) of the prior (e.g., default class...
mean_prior_class_complexity
The entropy of the class distribution of the prior (see prior_class_complexity), divided by the number of instances in the input data.
mean_recall
Unweighted(!) macro-average Recall. In macro-averaging, Recall is computed locally over each category first and then the average over all...
mean_weighted_area_under_roc_curve
The macro weighted (by class size) average area_under_ROC_curve (AUROC). In macro-averaging, AUROC is computed locally over each category first...
mean_weighted_f_measure
The macro weighted (by class size) average F-Measure. In macro-averaging, F-measure is computed locally over each category first and then the...
mean_weighted_precision
The macro weighted (by class size) average Precision. In macro-averaging, Precision is computed locally over each category first and then the...
mean_weighted_recall
The macro weighted (by class size) average Recall. In macro-averaging, Recall is computed locally over each category first and then the average...
precision
Precision is defined as the number of true positive (TP) predictions, divided by the sum of the number of true positives and false positives...
predictive_accuracy
The Predictive Accuracy is the percentage of instances that are classified correctly. Is it 1 - ErrorRate.
prior_class_complexity
Entropy, in bits, of the prior class distribution. Calculated by taking the sum of -log2(priorProb) over all instances, where priorProb is the prior...
prior_entropy
Entropy, in bits, of the prior class distribution. Calculated by taking the sum of -log2(priorProb) over all instances, where priorProb is the prior...
recall
Recall is defined as the number of true positive (TP) predictions, divided by the sum of the number of true positives and false negatives...
relative_absolute_error
The Relative Absolute Error (RAE) is the mean absolute error (MAE) divided by the mean prior absolute error (MPAE).
root_mean_prior_squared_error
The Root Mean Prior Squared Error (RMPSE) is the Root Mean Squared Error (RMSE) of the prior (e.g., the default class prediction).
root_mean_squared_error
The Root Mean Squared Error (RMSE) measures how close the model's predictions are to the actual target values. It is the square root of the Mean...
root_relative_squared_error
The Root Relative Squared Error (RRSE) is the Root Mean Squared Error (RMSE) divided by the Root Mean Prior Squared Error (RMPSE). See...
run_cpu_time
Runtime in seconds of the entire run. In the case of cross-validation runs, this will include all iterations.
run_memory
Amount of memory, in bytes, used during the entire run.
run_virtual_memory
Amount of virtual memory, in bytes, used during the entire run.
unclassified_instance_count
Number of instances that were not classified by the model.
webb_bias
Bias component (squared) of the bias-variance decomposition as defined by Webb in: Geoffrey I. Webb (2000), MultiBoosting: A Technique for...
webb_error
Intrinsic error component (squared) of the bias-variance decomposition as defined by Webb in: Geoffrey I. Webb (2000), MultiBoosting: A Technique...
webb_variance
Variance component of the bias-variance decomposition as defined by Webb in: Geoffrey I. Webb (2000), MultiBoosting: A Technique for Combining...

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Supervised Classification

Given a dataset with a classification target and a set of train/test splits, e.g. generated by a cross-validation procedure, train a model and return the predictions of that model.
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Given a dataset with a nominal target, various data samples of increasing size are defined. A model is build for each individual data sample; from this a learning curve can be drawn.
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