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satimage

satimage

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  • OpenML-CC18 OpenML100 study_1 study_123 study_14 study_34 study_37 study_41 study_52 study_7 study_70 study_76 study_98 study_99
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Author: Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)) - 1993 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels. The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel. Each pixel is categorized as one of the following classes: 1 red soil 2 cotton crop 3 grey soil 4 damp grey soil 5 soil with vegetation stubble 6 mixture class (all types present) 7 very damp grey soil NB. There are no examples with class 6 in this dataset. The data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset. ### Attribute information There are 36 predictive attributes (= 4 spectral bands x 9 pixels in neighborhood). In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary. In this version, the pixel values 0..255 are normalized around 0. Note: it is unclear why the attributes are named Aattr - Fattr in this version, since there are only 4 bands and 9 pixels, naming them A1, B1, C1, D1, A2, B2, C2, D2, ... would have made more sense.

37 features

class (target)nominal6 unique values
0 missing
Aattrnumeric51 unique values
0 missing
Battrnumeric84 unique values
0 missing
Cattrnumeric76 unique values
0 missing
Dattrnumeric102 unique values
0 missing
Eattrnumeric51 unique values
0 missing
Fattrnumeric82 unique values
0 missing
A1attrnumeric76 unique values
0 missing
B2attrnumeric103 unique values
0 missing
C3attrnumeric50 unique values
0 missing
D4attrnumeric81 unique values
0 missing
E5attrnumeric78 unique values
0 missing
F6attrnumeric104 unique values
0 missing
A7attrnumeric51 unique values
0 missing
B8attrnumeric83 unique values
0 missing
C9attrnumeric78 unique values
0 missing
D10attrnumeric101 unique values
0 missing
E11attrnumeric50 unique values
0 missing
F12attrnumeric80 unique values
0 missing
A13attrnumeric77 unique values
0 missing
B14attrnumeric104 unique values
0 missing
C15attrnumeric50 unique values
0 missing
D16attrnumeric80 unique values
0 missing
E17attrnumeric78 unique values
0 missing
F18attrnumeric104 unique values
0 missing
A19attrnumeric51 unique values
0 missing
B20attrnumeric82 unique values
0 missing
C21attrnumeric75 unique values
0 missing
D22attrnumeric102 unique values
0 missing
E23attrnumeric50 unique values
0 missing
F24attrnumeric81 unique values
0 missing
A25attrnumeric77 unique values
0 missing
B26attrnumeric103 unique values
0 missing
C27attrnumeric50 unique values
0 missing
D28attrnumeric80 unique values
0 missing
E29attrnumeric77 unique values
0 missing
F30attrnumeric104 unique values
0 missing

107 properties

6430
Number of instances (rows) of the dataset.
37
Number of attributes (columns) of the dataset.
6
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.
36
Number of numeric attributes.
1
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.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.82
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
6
Average number of distinct values among the attributes of the nominal type.
-0.52
First quartile of skewness among attributes of the numeric type.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.04
Mean skewness among attributes of the numeric type.
1
First quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.15
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.82
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
0.11
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
23.81
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.48
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.48
Entropy of the target attribute values.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1531
Number of instances belonging to the most frequent class.
-0.92
Minimum kurtosis among attributes of the numeric type.
-0
Second quartile (Median) of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.56
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.28
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.04
Second quartile (Median) of skewness among attributes of the numeric type.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
-0
Maximum of means among attributes of the numeric type.
6
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.67
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.17
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.
6
The maximum number of distinct values among attributes of the nominal type.
1
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.85
Third quartile of kurtosis among attributes of the numeric type.
0.19
Average class difference between consecutive instances.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.92
Maximum skewness among attributes of the numeric type.
9.72
Percentage of instances belonging to the least frequent class.
97.3
Percentage of numeric attributes.
-0
Third quartile of means among attributes of the numeric type.
0.93
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Maximum standard deviation of attributes of the numeric type.
625
Number of instances belonging to the least frequent class.
2.7
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.15
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.67
Third quartile of skewness among attributes of the numeric type.
0.82
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.15
Mean kurtosis among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.84
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
0.93
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

38 tasks

13458 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
308 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
203 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
368 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1321 runs - target_feature: class
1316 runs - target_feature: class
1313 runs - target_feature: class
1311 runs - target_feature: class
1309 runs - target_feature: class
1306 runs - target_feature: class
0 runs - target_feature: class
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0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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0 runs - target_feature: class
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0 runs - target_feature: class
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