Ensemble Regression (Random Forest, Gradient Boosting)
curl --request POST \
--url https://api.fincept.in/quantlib/ml/regression/ensemble \
--header 'Content-Type: application/json' \
--header 'X-API-Key: <api-key>' \
--data '
{
"X": [
[
1.2,
0.5,
3.1
],
[
2.1,
1.3,
2.5
],
[
0.8,
0.9,
4.2
]
],
"y": [
150000,
235000,
185000
],
"method": "gradient_boosting",
"n_estimators": 100,
"max_depth": 5,
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/regression/ensemble"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [150000, 235000, 185000],
"method": "gradient_boosting",
"n_estimators": 100,
"max_depth": 5,
"predict_X": [[1.5, 0.7, 3.3]]
}
headers = {
"X-API-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
X: [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
y: [150000, 235000, 185000],
method: 'gradient_boosting',
n_estimators: 100,
max_depth: 5,
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/regression/ensemble', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.fincept.in/quantlib/ml/regression/ensemble",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'X' => [
[
1.2,
0.5,
3.1
],
[
2.1,
1.3,
2.5
],
[
0.8,
0.9,
4.2
]
],
'y' => [
150000,
235000,
185000
],
'method' => 'gradient_boosting',
'n_estimators' => 100,
'max_depth' => 5,
'predict_X' => [
[
1.5,
0.7,
3.3
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-API-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.fincept.in/quantlib/ml/regression/ensemble"
payload := strings.NewReader("{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-API-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.fincept.in/quantlib/ml/regression/ensemble")
.header("X-API-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.fincept.in/quantlib/ml/regression/ensemble")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}"
response = http.request(request)
puts response.read_body{
"success": true,
"data": {
"predictions": [
197500
]
}
}{
"detail": "Invalid API key"
}{
"detail": "Insufficient credits. This endpoint requires 5 credits."
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}quantlib-ml
Ensemble Regression (Random Forest, Gradient Boosting)
Fits ensemble regression models for maximum predictive accuracy. Random Forest for variance reduction and robustness, Gradient Boosting for highest performance. Returns predictions and feature importances. [Tier: ENTERPRISE, Credits: 10]
POST
/
quantlib
/
ml
/
regression
/
ensemble
Ensemble Regression (Random Forest, Gradient Boosting)
curl --request POST \
--url https://api.fincept.in/quantlib/ml/regression/ensemble \
--header 'Content-Type: application/json' \
--header 'X-API-Key: <api-key>' \
--data '
{
"X": [
[
1.2,
0.5,
3.1
],
[
2.1,
1.3,
2.5
],
[
0.8,
0.9,
4.2
]
],
"y": [
150000,
235000,
185000
],
"method": "gradient_boosting",
"n_estimators": 100,
"max_depth": 5,
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/regression/ensemble"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [150000, 235000, 185000],
"method": "gradient_boosting",
"n_estimators": 100,
"max_depth": 5,
"predict_X": [[1.5, 0.7, 3.3]]
}
headers = {
"X-API-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
X: [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
y: [150000, 235000, 185000],
method: 'gradient_boosting',
n_estimators: 100,
max_depth: 5,
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/regression/ensemble', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.fincept.in/quantlib/ml/regression/ensemble",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'X' => [
[
1.2,
0.5,
3.1
],
[
2.1,
1.3,
2.5
],
[
0.8,
0.9,
4.2
]
],
'y' => [
150000,
235000,
185000
],
'method' => 'gradient_boosting',
'n_estimators' => 100,
'max_depth' => 5,
'predict_X' => [
[
1.5,
0.7,
3.3
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-API-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.fincept.in/quantlib/ml/regression/ensemble"
payload := strings.NewReader("{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-API-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.fincept.in/quantlib/ml/regression/ensemble")
.header("X-API-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.fincept.in/quantlib/ml/regression/ensemble")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"X\": [\n [\n 1.2,\n 0.5,\n 3.1\n ],\n [\n 2.1,\n 1.3,\n 2.5\n ],\n [\n 0.8,\n 0.9,\n 4.2\n ]\n ],\n \"y\": [\n 150000,\n 235000,\n 185000\n ],\n \"method\": \"gradient_boosting\",\n \"n_estimators\": 100,\n \"max_depth\": 5,\n \"predict_X\": [\n [\n 1.5,\n 0.7,\n 3.3\n ]\n ]\n}"
response = http.request(request)
puts response.read_body{
"success": true,
"data": {
"predictions": [
197500
]
}
}{
"detail": "Invalid API key"
}{
"detail": "Insufficient credits. This endpoint requires 5 credits."
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Authorizations
API key for authentication. Get your key at https://api.fincept.in/auth/register
Body
application/json
Feature matrix
Example:
[ [1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2] ]
Continuous target values
Example:
[150000, 235000, 185000]
Ensemble method
Available options:
random_forest, gradient_boosting Example:
"gradient_boosting"
Number of trees
Example:
100
Maximum tree depth
Example:
5
Optional feature matrix for prediction
Example:
[[1.5, 0.7, 3.3]]
⌘I
