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  • label_leakage OpenML100 study_123 study_135 study_14 study_144 study_34 study_52 study_293
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Author: Dr. Fernando Camacho Source: Unknown - 1995 Please cite: Camacho, F. and Arron, G. (1995) Effects of the regulators paclobutrazol and flurprimidol on the growth of terminal sprouts formed on trimmed silver maple trees. Canadian Journal of Statistics 3(23). Data on tree growth used in the Case Study published in the September, 1995 issue of the Canadian Journal of Statistics. This data set was been provided by Dr. Fernando Camacho, Ontario Hydro Technologies, 800 Kipling Ave, Toronto Canada M3Z 5S4. It forms the basis of the Case Study in Data Analysis published in the Canadian Journal of Statistics, September 1995. It can be freely used for noncommercial purposes, as long as proper acknowledgement to the source and to the Canadian Journal of Statistics is made. Description The effects of the Growth Regulators Paclobutrazol (PP 333) and Flurprimidol (EL-500) on the Number and Length of Internodes in Terminal Sprouts Formed on Trimmed Silver Maple Trees. Introduction: The trimming of trees under distribution lines on city streets and in rural areas is a major problem and expense for electrical utilities. Such operations are routinely performed at intervals of one to eight years depending upon the individual species growth rate and the amount of clearance required. Ontario Hydro trims about 500,000 trees per year at a cost of about $25 per tree. Much effort has been spent in developing chemicals for the horticultural industry to retard the growth of woody and herbaceous plants. Recently, a group of new growth regulators was introduced which was shown to be effective in controlling the growth of trees without producing noticeable injury symptoms. In this group are PP 333 ( common name paclobutrazol) (2RS, 3RS - 1 -(4-chlorophenyl) - 4,4 - dimethyl - 2 - (1,2,4-triazol-l-yl) pentan - 3- ol and EL-500 (common name flurprimidol and composition alpha - (1-methylethyl) - alpha - [4-(trifluromethoxyl) phenyl] - 5- pyrimidine - methanol). Both EL-500 and PP-333 have been reported to control excessive sprout growth in a number of species when applied as a foliar spray, as a soil drench, or by trunk injection. Sprout length is a function of both the number of internodes and the length of the individual internodes in the sprout. While there have been many reports that both PP 333 and EL-500 cause a reduction in the length of internodes formed in sprouts on woody plants treated with the growth regulators, there has been but one report that EL-500 application to apple trees resulted in a reduction of the number of internodes formed per sprout. The purpose of the present study was to investigate the length of the terminal sprouts, the length of the individual internodes in those sprouts, and the number of internodes in trimmed silver maple trees following trunk injection with the growth regulators PP 333 and EL-500. Experimental Details. Multistemmed 12-year-old silver maple trees growing at Wesleyville, Ontario were trunk injected with methanolic solutions of EL-500 and PP-333 in May of 1985 using a third generation Asplundh tree injector. Two different application rates (20 g/L and 4 g/L) were used for each chemical. The volume of solution (and hence the amount of active ingredient) injected into each tree was determined from the diameter of the tree, using the formula: vol(mL) = (dbh)*(dbh)*.492 where dbh is the diameter at breast height. Two sets of control trees were included in the experiment. In one set, tree received no injection (control) and in a second set, the trees were injected with methanol, the carrier in the growth regulator solutions. Ten trees, chosen at random, were used in each of the control and experimental sets. Prior to injection, all the trees were trimmed by a forestry crew, with their heights being reduced by about one third. In January 1987, twenty months after the trees were injected, between six and eight limbs were removed at random from the bottom two-thirds of the canopy of each of the ten trees in each experimental and control set. The limbs were returned to the laboratory and the length of all the terminal sprouts, the lengths of the individual internodes, and the number of internodes recorded. Between one and 25 terminal sprouts were found on each limb collected. Sprouts which had a length of 1 cm or less were recorded as being 1 cm in length. In such spouts, the internode lengths were not measured, but were calculated from the total length of the sprout and the number of internodes counted. Internode lengths were then expressed to one decimal place. In two instances, one of the ten trees in a set could not be sampled because limb removal would have jeopardized the health of the tree over the long-term. Data set: Each of the records represents a terminal sprout and contains the following information: N the sprout number TR treatment 1 control 2 methanol control 3 PP 333 20g/L 4 PP 333 4g/L 5 EL 500 20g/L 6 EL 500 4g/L TREE tree id BR branch id TL total sprout length (cm) IN number of internodes on the sprout INTER a list of the lengths of the internodes in the sprout, starting from the base of the sprout (129 entries) Sprouts 1868 to 1879 do not have branch identification data.

35 features

TR (target)nominal6 unique values
0 missing
INTERNODE_14numeric72 unique values
2653 missing
INTERNODE_29numeric1 unique values
2795 missing
INTERNODE_15numeric66 unique values
2660 missing
INTERNODE_16numeric52 unique values
2678 missing
INTERNODE_17numeric51 unique values
2711 missing
INTERNODE_18numeric43 unique values
2725 missing
INTERNODE_19numeric35 unique values
2740 missing
INTERNODE_20numeric23 unique values
2753 missing
INTERNODE_21numeric18 unique values
2770 missing
INTERNODE_22numeric10 unique values
2779 missing
INTERNODE_23numeric9 unique values
2786 missing
INTERNODE_24numeric7 unique values
2789 missing
INTERNODE_25numeric3 unique values
2792 missing
INTERNODE_26numeric1 unique values
2795 missing
INTERNODE_27numeric1 unique values
2795 missing
INTERNODE_28numeric1 unique values
2795 missing
N (row identifier)numeric2796 unique values
0 missing
INTERNODE_5numeric113 unique values
1999 missing
TREEnominal57 unique values
0 missing
BRnominal10 unique values
12 missing
TLnumeric170 unique values
0 missing
INnumeric26 unique values
0 missing
INTERNODE_1numeric30 unique values
0 missing
INTERNODE_2numeric68 unique values
64 missing
INTERNODE_3numeric117 unique values
637 missing
INTERNODE_4numeric128 unique values
1634 missing
INTERNODE_13numeric76 unique values
2634 missing
INTERNODE_6numeric115 unique values
2172 missing
INTERNODE_7numeric99 unique values
2308 missing
INTERNODE_8numeric95 unique values
2406 missing
INTERNODE_9numeric86 unique values
2489 missing
INTERNODE_10numeric86 unique values
2543 missing
INTERNODE_11numeric85 unique values
2578 missing
INTERNODE_12numeric78 unique values
2608 missing
Show all 35 features

107 properties

NumberOfInstances
2796
Number of instances (rows) of the dataset.
NumberOfFeatures
35
Number of attributes (columns) of the dataset.
NumberOfClasses
6
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
68100
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
2795
Number of instances with at least one value missing.
NumberOfNumericFeatures
32
Number of numeric attributes.
NumberOfSymbolicFeatures
3
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
1
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
CfsSubsetEval_DecisionStumpErrRate
0
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
CfsSubsetEval_DecisionStumpKappa
1
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
CfsSubsetEval_NaiveBayesAUC
1
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
CfsSubsetEval_NaiveBayesErrRate
0
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
0
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
CfsSubsetEval_kNN1NKappa
1
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
ClassEntropy
2.52
Entropy of the target attribute values.
DecisionStumpAUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.71
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
1.96
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
24.32
Percentage of instances belonging to the most frequent class.
MajorityClassSize
680
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
5.58
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
88.43
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
9.33
Maximum of means among attributes of the numeric type.
MaxMutualInformation
2.52
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
57
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
6.53
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
21.93
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
4.29
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
5.42
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
2.81
Mean of means among attributes of the numeric type.
MeanMutualInformation
1.29
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
2.33
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
24.33
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.32
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
2.56
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
3.01
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.12
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.3
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.05
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
6
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.13
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.8
Percentage of instances belonging to the least frequent class.
MinorityClassSize
274
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
99.96
Percentage of instances having missing values.
PercentageOfMissingValues
69.59
Percentage of missing values.
PercentageOfNumericFeatures
91.43
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
8.57
Percentage of nominal attributes.
Quartile1AttributeEntropy
3.01
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.53
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1.19
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.05
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.41
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.9
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
4.29
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
2.99
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
1.29
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.72
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
2.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
5.58
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.98
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
3.89
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
2.52
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.99
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
2.89
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
28.36
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.81
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

39 tasks

Supervised Classification on cjs
10925 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: TR
Supervised Classification on cjs
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: TR
Learning Curve on cjs
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: TR
Clustering on cjs
0 runs - target_feature: TR
Clustering on cjs
0 runs
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Clustering on cjs
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on cjs
1300 runs - target_feature: TR
Subgroup Discovery on cjs
1299 runs - target_feature: TR
Subgroup Discovery on cjs
1298 runs - target_feature: TR
Subgroup Discovery on cjs
1298 runs - target_feature: TR
Subgroup Discovery on cjs
1297 runs - target_feature: TR
Subgroup Discovery on cjs
1297 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
Subgroup Discovery on cjs
0 runs - target_feature: TR
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