NaiveBayes - Algorithm by weka

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This is the NaiveBayes classifier, as implemented in Weka: http://weka.sourceforge.net/doc.stable/weka/classifiers/bayes/NaiveBayes.html Send your input to the algorithm as a simple Json object, with the following options: mode: One of train/update/load. Use "train" to train a new classifier , use "update" if you have new instances to train an existing updateable classifier, use "load" to load an existing classifier for classifying test data trainUrl: The path to the training data that you uploaded to our Data API testUrl: The path to the test data that you would like to get the labels using this classifier (optional if you would like to use the cross-validation option) modelUrl: The path that you would like to either save the model that is trained or if you would like to load an already trained and saved model cv: The number of cross-validation folds that you would like to use options: A string that contains any options that you would like to specifically set for this classifier (format: param name followed by desired value separated by space: e.g. "-C 5 -t 2"). For options specific to this classifier, please see Wekadocs linked above classIndex: Specify the index of the class values in the test set ( caution: When using a test set, assumes that the class values are at the last index if the classIndex parameter is not specified, so please arrange accordingly)

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Calls other algorithms

This algorithm has permission to call other algorithms. This allows an algorithm to compose sophisticated functionality using other algorithms as building blocks, however it also carries the potential of incurring additional royalty and usage costs from any algorithm that it calls.

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1. Type your input

2. See the result

Running algorithm...

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/weka/NaiveBayes/0.1.1

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://weka/NaiveBayes/0.1.1 -d '{{input | formatInput:"cli"}}'

View CLI Docs

import (
  algorithmia "github.com/algorithmiaio/algorithmia-go"
)

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://weka/NaiveBayes/0.1.1")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
fmt.Println(response.Result)

View Go Docs

import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://weka/NaiveBayes/0.1.1");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());

View Java Docs

import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://weka/NaiveBayes/0.1.1")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)

View Scala Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://weka/NaiveBayes/0.1.1")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });

View Javascript Docs

using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://weka/NaiveBayes/0.1.1");
var response = algorithm.pipe<object>(input);
Console.WriteLine(response.result);

View .NET/C# Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://weka/NaiveBayes/0.1.1")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });

View NodeJS Docs

import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('weka/NaiveBayes/0.1.1')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("weka/NaiveBayes/0.1.1")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('weka/NaiveBayes/0.1.1')
puts algo.pipe(input).result

View Ruby Docs

use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("weka/NaiveBayes/0.1.1");
let response = algo.pipe(input);

View Rust Docs

import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "weka/NaiveBayes/0.1.1") { resp, error in
  print(resp)
}

View Swift Docs

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