Public
Edited
Jun 6, 2023
2 stars
rfc = import("https://cdn.skypack.dev/random-forest-classifier@latest")
rf = new rfc.RandomForestClassifier({ n_estimators: 10 })
data = [
{
length: 5.1,
width: 3.5,
petal_length: 1.4,
petal_width: 0.2,
species: "setosa"
},
{
length: 6.5,
width: 3,
petal_length: 5.2,
petal_width: 2,
species: "virginica"
},
{
length: 6.6,
width: 3,
petal_length: 4.4,
petal_width: 1.4,
species: "versicolor"
}
]
testdata = [
{
length: 6.3,
width: 2.5,
petal_length: 5,
petal_width: 1.9
//"species":"virginica"
},
{
length: 4.7,
width: 3.2,
petal_length: 1.3,
petal_width: 0.2
//"species":"setosa"
}
]
rf.fit(data, null, "species", function (err, trees) {
//console.log(JSON.stringify(trees, null, 4));
var pred = rf.predict(testdata, trees);
// pred = ["virginica", "setosa"]
console.log({ rf, trees, pred });
for (const tree of rf.trees) {
const render = renderTreeNode(tree.model);
console.log(render);
}
})
function renderTreeNode(node, depth = 0) {
if (node.type === "result") {
// class name
return `\n${" ".repeat(depth * 2)} - ${depth} ${node.type} ${node.val}`;
} else {
// feature value to cut
let resultString = `\n${" ".repeat(depth * 2)} - ${depth} ${node.type} ${
node.name
} ${node.cut}`;
resultString += renderTreeNode(node.vals[0].child, depth + 1);
resultString += renderTreeNode(node.vals[1].child, depth + 1);
return resultString;
}
}
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