Data
tecator

tecator

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Author: Source: Unknown - Date unknown Please cite: This is the Tecator data set: The task is to predict the fat content of a meat sample on the basis of its near infrared absorbance spectrum. 1. Statement of permission from Tecator (the original data source) These data are recorded on a Tecator Infratec Food and Feed Analyzer working in the wavelength range 850 - 1050 nm by the Near Infrared Transmission (NIT) principle. Each sample contains finely chopped pure meat with different moisture, fat and protein contents. If results from these data are used in a publication we want you to mention the instrument and company name (Tecator) in the publication. In addition, please send a preprint of your article to Karin Thente, Tecator AB, Box 70, S-263 21 Hoganas, Sweden The data are available in the public domain with no responsability from the original data source. The data can be redistributed as long as this permission note is attached. For more information about the instrument - call Perstorp Analytical's representative in your area. 2. Description of the data file For each meat sample the data consists of a 100 channel spectrum of absorbances and the contents of moisture (water), fat and protein. The absorbance is -log10 of the transmittance measured by the spectrometer. The three contents, measured in percent, are determined by analytic chemistry. There are 240 samples which are divided into 5 data sets for the purpose of model validation and extrapolation studies. The data sets, further described in reference 1, are: Data set Use Samples C Traning 129 M Monitoring 43 T Testing 43 E1 Extrapolation, Fat 8 E2 Extrapolation, Protein 17 The data for all 240 samples appear at the end of this file - 25 lines per sample. The data sets appear in the order of the table above. The spectra are preprocessed using a principal component analysis on the data set C, and the first 22 principal components (scaled to unit variance) are included for each sample. Thus if you want to use the data for a standard (interpolation) test of your algorithm, use sample 1-172 for training and sample 173-215 for testing (and ignore the last 25 samples), and use the first 13 or so principal components to predict the fat content. Each line contains the 100 absorbances followed by the 22 principal components and finally the contents of moisture, fat and protein. Preceeding the data lines, the following lines appear: real_in=122 real_out=3 training_examples=172 test_examples=43 extrapolation_examples=25 3. More details on how to use the data The data are made available as a benchmark for regression models. In order to compare models, it is practical to use the data set as follows: C and M combined are used to tune (estimate, train) the model. (Some approaches set aside some training data to control overfitting. These data should be a subset of C+M. In (1) the subset M was used for this purpose.) T is used to test the model once it has been tuned. If each model has an element of randomness (as is the case for neural networks) the most reliable measure of performance of a single model is obtained by selecting a handful of models on the basis of C+M and quoting the average of the performances on T. In the presence of randomness it is bad practice to train a lot of models on C+M and then select the best of these on the basis of T. C, M and T are drawn from the same pool of data, so T is used to test the ability of the models to interpolate. The data sets E1 and E2 contain more fat and protein respectively and are intended to be used to test the ability of the models to extrapolate. 4. Performance of neural network models The performance is measured as Standard Error of Prediction (SEP) which is the root mean square of the difference between the true and the predicted content. For the prediction of fat on the data set T the following results were obtained Reference SEP method (see the papers for details) (1) 0.65 10-6-1 network, early stopping (2) 0.52 10-3-1 network, Bayesian (3) 0.36 13-X-1 network, Bayesian, Automatic Relevance Determination A linear model with 10 inputs yields SEP=2.78. 5. References (1) C.Borggaard and H.H.Thodberg, "Optimal Minimal Neural Interpretation of Spectra", Analytical Chemistry 64 (1992), p 545-551. (2) H.H.Thodberg, "Ace of Bayes: Application of Neural Networks with Pruning" Manuscript 1132, Danish Meat Research Institute (1993), available by anonymous ftp in the file: pub/neuroprose/thodberg.ace-of-bayes.ps.Z on the Internet node archive.cis.ohio-state.edu (128.146.8.52). (3) Revised and extended version of (2), in preparation, to be submitted to IEEE Trans. Neural Networks (1995) available by anonymous ftp in the file: pub/neuroprose/thodberg.bayesARD.ps.Z on the Internet node archive.cis.ohio-state.edu (128.146.8.52). Hans Henrik Thodberg Email: thodberg@nn.dmri.dk Danish Meat Research Institute Phone: (+45) 42 36 12 00 Maglegaardsvej 2, Postboks 57 Fax: (+45) 42 36 48 36 DK-4000 Roskilde, Denmark real_in=122 real_out=3 training_examples=172 test_examples=43 extrapolation_examples=25 Note: all 240 samples are included in the same order as mentioned above Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

125 features

fat (target)numeric157 unique values
0 missing
absorbance_1numeric216 unique values
0 missing
absorbance_2numeric216 unique values
0 missing
absorbance_3numeric216 unique values
0 missing
absorbance_4numeric214 unique values
0 missing
absorbance_5numeric216 unique values
0 missing
absorbance_6numeric216 unique values
0 missing
absorbance_7numeric216 unique values
0 missing
absorbance_8numeric216 unique values
0 missing
absorbance_9numeric216 unique values
0 missing
absorbance_10numeric216 unique values
0 missing
absorbance_11numeric216 unique values
0 missing
absorbance_12numeric215 unique values
0 missing
absorbance_13numeric216 unique values
0 missing
absorbance_14numeric216 unique values
0 missing
absorbance_15numeric216 unique values
0 missing
absorbance_16numeric216 unique values
0 missing
absorbance_17numeric216 unique values
0 missing
absorbance_18numeric216 unique values
0 missing
absorbance_19numeric215 unique values
0 missing
absorbance_20numeric216 unique values
0 missing
absorbance_21numeric215 unique values
0 missing
absorbance_22numeric216 unique values
0 missing
absorbance_23numeric216 unique values
0 missing
absorbance_24numeric216 unique values
0 missing
absorbance_25numeric215 unique values
0 missing
absorbance_26numeric216 unique values
0 missing
absorbance_27numeric216 unique values
0 missing
absorbance_28numeric216 unique values
0 missing
absorbance_29numeric215 unique values
0 missing
absorbance_30numeric216 unique values
0 missing
absorbance_31numeric216 unique values
0 missing
absorbance_32numeric215 unique values
0 missing
absorbance_33numeric216 unique values
0 missing
absorbance_34numeric216 unique values
0 missing
absorbance_35numeric215 unique values
0 missing
absorbance_36numeric216 unique values
0 missing
absorbance_37numeric216 unique values
0 missing
absorbance_38numeric216 unique values
0 missing
absorbance_39numeric216 unique values
0 missing
absorbance_40numeric215 unique values
0 missing
absorbance_41numeric216 unique values
0 missing
absorbance_42numeric216 unique values
0 missing
absorbance_43numeric216 unique values
0 missing
absorbance_44numeric216 unique values
0 missing
absorbance_45numeric216 unique values
0 missing
absorbance_46numeric216 unique values
0 missing
absorbance_47numeric216 unique values
0 missing
absorbance_48numeric216 unique values
0 missing
absorbance_49numeric216 unique values
0 missing
absorbance_50numeric216 unique values
0 missing
absorbance_51numeric216 unique values
0 missing
absorbance_52numeric216 unique values
0 missing
absorbance_53numeric216 unique values
0 missing
absorbance_54numeric216 unique values
0 missing
absorbance_55numeric215 unique values
0 missing
absorbance_56numeric215 unique values
0 missing
absorbance_57numeric215 unique values
0 missing
absorbance_58numeric216 unique values
0 missing
absorbance_59numeric215 unique values
0 missing
absorbance_60numeric216 unique values
0 missing
absorbance_61numeric216 unique values
0 missing
absorbance_62numeric216 unique values
0 missing
absorbance_63numeric216 unique values
0 missing
absorbance_64numeric216 unique values
0 missing
absorbance_65numeric216 unique values
0 missing
absorbance_66numeric216 unique values
0 missing
absorbance_67numeric216 unique values
0 missing
absorbance_68numeric215 unique values
0 missing
absorbance_69numeric216 unique values
0 missing
absorbance_70numeric216 unique values
0 missing
absorbance_71numeric216 unique values
0 missing
absorbance_72numeric216 unique values
0 missing
absorbance_73numeric216 unique values
0 missing
absorbance_74numeric216 unique values
0 missing
absorbance_75numeric216 unique values
0 missing
absorbance_76numeric216 unique values
0 missing
absorbance_77numeric216 unique values
0 missing
absorbance_78numeric216 unique values
0 missing
absorbance_79numeric216 unique values
0 missing
absorbance_80numeric216 unique values
0 missing
absorbance_81numeric215 unique values
0 missing
absorbance_82numeric216 unique values
0 missing
absorbance_83numeric216 unique values
0 missing
absorbance_84numeric216 unique values
0 missing
absorbance_85numeric216 unique values
0 missing
absorbance_86numeric215 unique values
0 missing
absorbance_87numeric216 unique values
0 missing
absorbance_88numeric216 unique values
0 missing
absorbance_89numeric216 unique values
0 missing
absorbance_90numeric216 unique values
0 missing
absorbance_91numeric216 unique values
0 missing
absorbance_92numeric216 unique values
0 missing
absorbance_93numeric215 unique values
0 missing
absorbance_94numeric214 unique values
0 missing
absorbance_95numeric216 unique values
0 missing
absorbance_96numeric216 unique values
0 missing
absorbance_97numeric216 unique values
0 missing
absorbance_98numeric216 unique values
0 missing
absorbance_99numeric216 unique values
0 missing
absorbance_100numeric216 unique values
0 missing
principal_component_1numeric216 unique values
0 missing
principal_component_2numeric216 unique values
0 missing
principal_component_3numeric217 unique values
0 missing
principal_component_4numeric217 unique values
0 missing
principal_component_5numeric216 unique values
0 missing
principal_component_6numeric216 unique values
0 missing
principal_component_7numeric216 unique values
0 missing
principal_component_8numeric216 unique values
0 missing
principal_component_9numeric216 unique values
0 missing
principal_component_10numeric216 unique values
0 missing
principal_component_11numeric216 unique values
0 missing
principal_component_12numeric216 unique values
0 missing
principal_component_13numeric216 unique values
0 missing
principal_component_14numeric217 unique values
0 missing
principal_component_15numeric216 unique values
0 missing
principal_component_16numeric216 unique values
0 missing
principal_component_17numeric216 unique values
0 missing
principal_component_18numeric217 unique values
0 missing
principal_component_19numeric216 unique values
0 missing
principal_component_20numeric216 unique values
0 missing
principal_component_21numeric216 unique values
0 missing
principal_component_22numeric216 unique values
0 missing
moisturenumeric141 unique values
0 missing
proteinnumeric97 unique values
0 missing

107 properties

240
Number of instances (rows) of the dataset.
125
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
125
Number of numeric attributes.
0
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Average number of distinct values among the attributes of the nominal type.
0.79
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.69
Mean skewness among attributes of the numeric type.
0.51
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.83
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
-0.47
Minimum kurtosis among attributes of the numeric type.
3.09
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.15
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
9.36
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.82
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
62.85
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.55
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-1.55
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
The maximum number of distinct values among attributes of the nominal type.
0.41
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.83
Third quartile of kurtosis among attributes of the numeric type.
-2.64
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.69
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.
3.42
Third quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
14.36
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.91
Third quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.01
Mean kurtosis among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.58
First quartile of kurtosis among attributes of the numeric type.
0.55
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.36
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.85
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

4 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: fat
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: fat
0 runs - estimation_procedure: 50 times Clustering
Define a new task