Playing with smile and iris dataset / published by https://github.com/dacr/code-examples-manager #81cddd4b-a3e6-409b-b835-d42b6b653fc0/ad847c7cf72d3d9fac41cd8e23b6032a1cc766b8

iris.sc

// summary : Playing with smile and iris dataset
// keywords : smile, machine-learning, iris, ai
// publish : gist
// authors : David Crosson
// license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2)
// id : 81cddd4b-a3e6-409b-b835-d42b6b653fc0
// created-on : 2021-03-05T09:23:01Z
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

// ---------------------
//> using scala "3.4.2"
//> using dep "com.github.pathikrit::better-files:3.9.2"
//> using dep "com.github.haifengl::smile-scala:3.1.1"
//> using dep "org.bytedeco:javacpp-platform:1.5.10"
//> using dep "org.bytedeco:javacpp:1.5.10,classifier=linux-x86_64"
//> using dep "org.bytedeco:arpack-ng:3.9.1-1.5.10,classifier=linux-x86_64"
//> using dep "org.bytedeco:openblas:0.3.26-1.5.10,classifier=linux-x86_64"
//> using dep "org.slf4j:slf4j-nop:2.0.13"
//> using dep "com.lihaoyi::requests:0.9.0"
// ---------------------

import scala.language.postfixOps
import better.files.*
import smile.read
import smile.util.*
import smile.math.*
import smile.math.MathEx.*
import smile.math.distance.*
import smile.data.*
import smile.data.formula.*
import smile.data.measure.*
import smile.data.`type`.*

import smile.regression.*
import smile.stat.distribution.*
import smile.io.*
import smile.stat.Sampling


val inputFileName = "iris.arff"
val inputFile = inputFileName.toFile
if (inputFile.notExists) {
  val url = "https://gist.githubusercontent.com/myui/143fa9d05bd6e7db0114/raw/500f178316b802f1cade6e3bf8dc814a96e84b1e/iris.arff"
  for {out <- inputFile.newOutputStream.autoClosed} {requests.get(url).writeBytesTo(out)}
}

val iris = read.arff("iris.arff")

println(iris.summary())

val model = randomForest("class".~(), iris)

println(model.metrics)

val df = iris(0)
println(df)
println(model.predict(df))

println(df)
// TODO - to be continued...

println("enter to exit"); scala.io.StdIn.readLine()