Random Forest Apply

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Overview This routine applies a previously learned Mahout Random Forest Classifier to a set of test data. It takes as input a JSON array of four items, the first three are Data API URLs, of the (unlabeled) test data, the model file, and the (labelled) data used to train the model file, respectively, followed by a descriptor that details the type of each field in the dataset. It outputs the predicted labels of each instance in the test set. Note that test and training files are assumed to be CSVs. Data Format and Descriptor We assume that the first entry of any instance is the label, though Mahout does support other placement. The descriptor must be of form "L X X X ...", where each X designates the type of its respective field, either I (ignored), N (numerical), or C (categorical). L designates the label label. Think of the descriptor as a header for the data. As an example, a dataset with four attributes (beyond the label) might have the first two as categorical, the third numerical, and the last ignored, and its header would be "L C C N I". With the test data we don’t have a label and as a matter of convention Mahout expects us to pass a "-". Our code currently handles this as long as your data has the label as the first field, and this field is missing in test data.

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1. Type your input

2. See the result

Running algorithm...

3. Use this 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/mahout/RandomForestApply/0.3.5
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://mahout/RandomForestApply/0.3.5 -d '{{input | formatInput:"cli"}}'
View CLI 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://mahout/RandomForestApply/0.3.5");
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://mahout/RandomForestApply/0.3.5")
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://mahout/RandomForestApply/0.3.5")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mahout/RandomForestApply/0.3.5")
           .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('mahout/RandomForestApply/0.3.5')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("mahout/RandomForestApply/0.3.5")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mahout/RandomForestApply/0.3.5')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo('mahout/RandomForestApply/0.3.5');
let response = algo.pipe(input);
View Rust Docs
Discussion
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