fix documentation
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Antonio Garrote
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1 changed files with 82 additions and 67 deletions
149
README.markdown
149
README.markdown
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@ -21,7 +21,7 @@ Loading data from a CSV file:
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(use 'clj-ml.data)
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; Defining a dataset
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(def ds (make-dataset ; name of the dataset
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(def ds (make-dataset ; name of the dataset
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"name"
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; two numeric attributes and one nominal
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[:length :width {:kind [:good :bad]}]
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@ -30,23 +30,27 @@ Loading data from a CSV file:
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[24 53 :bad] ]))
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ds
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>#<ClojureInstances @relation name
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>
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>@attribute length numeric
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>@attribute width numeric
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>@attribute kind {good,bad}
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>
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>@data
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>12,34,good
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>24,53,bad>
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#<ClojureInstances @relation name
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@attribute length numeric
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@attribute width numeric
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@attribute kind {good,bad}
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@data
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12,34,good
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24,53,bad>
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; Using datasets like sequences
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(dataset-seq ds)
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>(#<Instance 12,34,good> #<Instance 24,53,bad>)
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(#<Instance 12,34,good> #<Instance 24,53,bad>)
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; Transforming instances into maps or vectors
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(instance-to-map (first (dataset-seq ds)))
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>{:kind :good, :width 34.0, :length 12.0}
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{:kind :good, :width 34.0, :length 12.0}
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(instance-to-vector (dataset-at ds 0))
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* Filtering datasets
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@ -75,71 +79,79 @@ Loading data from a CSV file:
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; Training the classifier
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(classifier-train classifier ds)
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>#<J48 J48 pruned tree
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>------------------
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>
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>petalwidth <= 0.6: Iris-setosa (50.0)
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>petalwidth > 0.6
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>| petalwidth <= 1.7
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>| | petallength <= 4.9: Iris-versicolor (48.0/1.0)
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>| | petallength > 4.9
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>| | | petalwidth <= 1.5: Iris-virginica (3.0)
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>| | | petalwidth > 1.5: Iris-versicolor (3.0/1.0)
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>| petalwidth > 1.7: Iris-virginica (46.0/1.0)
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>
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>Number of Leaves : 5
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>
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>Size of the tree : 9
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#<J48 J48 pruned tree
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------------------
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petalwidth <= 0.6: Iris-setosa (50.0)
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petalwidth > 0.6
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| petalwidth <= 1.7
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| | petallength <= 4.9: Iris-versicolor (48.0/1.0)
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| | petallength > 4.9
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| | | petalwidth <= 1.5: Iris-virginica (3.0)
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| | | petalwidth > 1.5: Iris-versicolor (3.0/1.0)
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| petalwidth > 1.7: Iris-virginica (46.0/1.0)
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Number of Leaves : 5
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Size of the tree : 9
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; We evaluate the classifier using a test dataset
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; last parameter should be a different test dataset, here we are using the same
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(def evaluation (classifier-evaluate classifier :dataset ds ds))
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>=== Confusion Matrix ===
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>
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> a b c <-- classified as
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> 50 0 0 | a = Iris-setosa
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> 0 49 1 | b = Iris-versicolor
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> 0 2 48 | c = Iris-virginica
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>
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>=== Summary ===
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>
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>Correctly Classified Instances 147 98 %
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>Incorrectly Classified Instances 3 2 %
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>Kappa statistic 0.97
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>Mean absolute error 0.0233
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>Root mean squared error 0.108
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>Relative absolute error 5.2482 %
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>Root relative squared error 22.9089 %
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>Total Number of Instances 150
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=== Confusion Matrix ===
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a b c <-- classified as
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50 0 0 | a = Iris-setosa
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0 49 1 | b = Iris-versicolor
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0 2 48 | c = Iris-virginica
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=== Summary ===
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Correctly Classified Instances 147 98 %
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Incorrectly Classified Instances 3 2 %
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Kappa statistic 0.97
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Mean absolute error 0.0233
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Root mean squared error 0.108
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Relative absolute error 5.2482 %
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Root relative squared error 22.9089 %
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Total Number of Instances 150
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(:kappa evaluation)
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>0.97
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0.97
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(:root-mean-squared-error e)
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>0.10799370769526968
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0.10799370769526968
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(:precision e)
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>{:Iris-setosa 1.0, :Iris-versicolor 0.9607843137254902, :Iris-virginica
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0.9795918367346939}
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{:Iris-setosa 1.0, :Iris-versicolor 0.9607843137254902, :Iris-virginica
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0.9795918367346939}
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; The classifier can also be evaluated using cross-validation
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(classifier-evaluate classifier :cross-validation ds 10)
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>=== Confusion Matrix ===
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>
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> a b c <-- classified as
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> 49 1 0 | a = Iris-setosa
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> 0 47 3 | b = Iris-versicolor
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> 0 4 46 | c = Iris-virginica
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>
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>=== Summary ===
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>
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>Correctly Classified Instances 142 94.6667 %
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>Incorrectly Classified Instances 8 5.3333 %
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>Kappa statistic 0.92
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>Mean absolute error 0.0452
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>Root mean squared error 0.1892
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>Relative absolute error 10.1707 %
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>Root relative squared error 40.1278 %
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>Total Number of Instances 150
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=== Confusion Matrix ===
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a b c <-- classified as
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49 1 0 | a = Iris-setosa
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0 47 3 | b = Iris-versicolor
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0 4 46 | c = Iris-virginica
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=== Summary ===
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Correctly Classified Instances 142 94.6667 %
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Incorrectly Classified Instances 8 5.3333 %
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Kappa statistic 0.92
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Mean absolute error 0.0452
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Root mean squared error 0.1892
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Relative absolute error 10.1707 %
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Root relative squared error 40.1278 %
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Total Number of Instances 150
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; A trained classifier can be used to classify new instances
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(def to-classify (make-instance ds
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@ -149,9 +161,12 @@ Loading data from a CSV file:
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:sepalwidth 3.5,
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:sepallength 5.1}))
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(classifier-classify classifier to-classify)
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> 0.0
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0.0
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(classifier-label to-classify)
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>#<Instance 5.1,3.5,1.4,0.2,Iris-setosa>
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#<Instance 5.1,3.5,1.4,0.2,Iris-setosa>
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; The classifiers can be saved and restored later
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