Tree-Based Regression (Decision Tree, Gradient Boosting)
curl --request POST \
--url https://api.fincept.in/quantlib/ml/regression/tree \
--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": [
0.35,
0.42,
0.28
],
"method": "gradient_boosting",
"max_depth": 3,
"n_estimators": 50,
"learning_rate": 0.05,
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/regression/tree"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [0.35, 0.42, 0.28],
"method": "gradient_boosting",
"max_depth": 3,
"n_estimators": 50,
"learning_rate": 0.05,
"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: [0.35, 0.42, 0.28],
method: 'gradient_boosting',
max_depth: 3,
n_estimators: 50,
learning_rate: 0.05,
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/regression/tree', 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/tree",
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' => [
0.35,
0.42,
0.28
],
'method' => 'gradient_boosting',
'max_depth' => 3,
'n_estimators' => 50,
'learning_rate' => 0.05,
'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/tree"
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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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/tree")
.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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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/tree")
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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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": [
0.38
],
"feature_importances": [
0.45,
0.32,
0.23
]
}
}{
"detail": "Invalid API key"
}{
"detail": "Insufficient credits. This endpoint requires 5 credits."
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}quantlib-ml
Tree-Based Regression (Decision Tree, Gradient Boosting)
Fits tree-based regression models. Decision trees for interpretability and non-linear relationships, gradient boosting for maximum predictive power. Returns predictions and optional feature importances. [Tier: ENTERPRISE, Credits: 10]
POST
/
quantlib
/
ml
/
regression
/
tree
Tree-Based Regression (Decision Tree, Gradient Boosting)
curl --request POST \
--url https://api.fincept.in/quantlib/ml/regression/tree \
--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": [
0.35,
0.42,
0.28
],
"method": "gradient_boosting",
"max_depth": 3,
"n_estimators": 50,
"learning_rate": 0.05,
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/regression/tree"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [0.35, 0.42, 0.28],
"method": "gradient_boosting",
"max_depth": 3,
"n_estimators": 50,
"learning_rate": 0.05,
"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: [0.35, 0.42, 0.28],
method: 'gradient_boosting',
max_depth: 3,
n_estimators: 50,
learning_rate: 0.05,
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/regression/tree', 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/tree",
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' => [
0.35,
0.42,
0.28
],
'method' => 'gradient_boosting',
'max_depth' => 3,
'n_estimators' => 50,
'learning_rate' => 0.05,
'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/tree"
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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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/tree")
.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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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/tree")
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 0.35,\n 0.42,\n 0.28\n ],\n \"method\": \"gradient_boosting\",\n \"max_depth\": 3,\n \"n_estimators\": 50,\n \"learning_rate\": 0.05,\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": [
0.38
],
"feature_importances": [
0.45,
0.32,
0.23
]
}
}{
"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:
[0.35, 0.42, 0.28]
Tree method
Available options:
tree, gradient_boosting Example:
"gradient_boosting"
Maximum tree depth
Example:
3
Number of trees (for gradient boosting)
Example:
50
Learning rate (for gradient boosting)
Example:
0.05
Optional feature matrix for prediction
Example:
[[1.5, 0.7, 3.3]]
⌘I
