Published
Edited
Aug 16, 2019
params=d3.range(1000).map(n => ({
age: Math.floor(Math.random()*80),
gender: Math.random() > 0.5 ? 'M' : 'F',
appts: Math.floor(Math.log(1/Math.random()))
}))
trainingData=params.map(({age, gender, appts}) => [age, gender, appts])
method = ({age, gender, appts}) =>
(gender == 'M' ?
(appts == 0 ? 10 : 0) + (age < 5 ? 20 : age > 60 ? 40 : 10) :
(appts == 0 ? 10 : 0) + (age < 10 ? 25 : age > 65 ? 50 : 15)
)// + Math.floor(Math.random()*10-5)
results=params.map(method)
regression = new ml.DecisionTreeRegression(options)
regression.train(trainingData, results)
arrAvg = arr => arr.reduce((a,b) => a + b, 0) / arr.length
arrAvg(results)
regression.predict([[25, 'M', 3]])//, [85, 'F', 0]])
x = d3.range(100).map(d => [d * 0.01, d*0.02])
y = x.map(d => Math.sin(d[0]))
reg = new ml.DecisionTreeRegression()
reg.train(x, y)
estimations = reg.predict(x)
exampleTest = ['Rainy'//, 'Hot', 'High', 'False'],
,'Rainy'//, 'Hot', 'High', 'True'],
,'Overcast'//, 'Hot', 'High', 'False'],
,'Sunny'//, 'Mild', 'High', 'False'],
,'Sunny'//, 'Cool', 'Normal', 'False'],
,'Sunny'//, 'Cool', 'Normal', 'True'],
,'Overcast'//, 'Cool', 'Normal', 'True'],
,'Rainy'//, 'Mild', 'High', 'False'],
,'Rainy'//, 'Cool', 'Normal', 'False'],
,'Sunny'//, 'Mild', 'Normal', 'False'],
,'Rainy'//, 'Mild', 'Normal', 'True'],
,'Overcast'//, 'Mild', 'High', 'True'],
,'Overcast'//, 'Hot', 'Normal', 'False'],
,'Sunny']//, 'Mild', 'High', 'True']]
exampleResults = [26,30,48,46,62,23,43,35,38,48,48,62,44,30]
exampleRegression = new ml.DecisionTreeRegression();
exampleRegression.train(exampleTest, exampleResults)
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