Logistic Regression for Credit Scoring
curl --request POST \
--url https://api.fincept.in/quantlib/ml/credit/logistic-regression \
--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,
1,
0
],
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/credit/logistic-regression"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [0, 1, 0],
"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, 1, 0],
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/credit/logistic-regression', 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/credit/logistic-regression",
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,
1,
0
],
'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/credit/logistic-regression"
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,\n 1,\n 0\n ],\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/credit/logistic-regression")
.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,\n 1,\n 0\n ],\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/credit/logistic-regression")
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,\n 1,\n 0\n ],\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": {
"coefficients": [
0.342,
-0.156,
0.871
],
"intercept": -1.234,
"aic": 145.67,
"bic": 152.89,
"predictions": [
0.234
]
}
}{
"detail": "Invalid API key"
}{
"detail": "Insufficient credits. This endpoint requires 5 credits."
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}quantlib-ml
Logistic Regression for Credit Scoring
Fits a logistic regression model for binary classification, commonly used for credit default prediction and PD modeling. Returns model coefficients, AIC, BIC, and optional predictions. Use this for developing credit scorecards and probability of default (PD) models. [Tier: ENTERPRISE, Credits: 10]
POST
/
quantlib
/
ml
/
credit
/
logistic-regression
Logistic Regression for Credit Scoring
curl --request POST \
--url https://api.fincept.in/quantlib/ml/credit/logistic-regression \
--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,
1,
0
],
"predict_X": [
[
1.5,
0.7,
3.3
]
]
}
'import requests
url = "https://api.fincept.in/quantlib/ml/credit/logistic-regression"
payload = {
"X": [[1.2, 0.5, 3.1], [2.1, 1.3, 2.5], [0.8, 0.9, 4.2]],
"y": [0, 1, 0],
"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, 1, 0],
predict_X: [[1.5, 0.7, 3.3]]
})
};
fetch('https://api.fincept.in/quantlib/ml/credit/logistic-regression', 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/credit/logistic-regression",
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,
1,
0
],
'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/credit/logistic-regression"
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,\n 1,\n 0\n ],\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/credit/logistic-regression")
.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,\n 1,\n 0\n ],\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/credit/logistic-regression")
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,\n 1,\n 0\n ],\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": {
"coefficients": [
0.342,
-0.156,
0.871
],
"intercept": -1.234,
"aic": 145.67,
"bic": 152.89,
"predictions": [
0.234
]
}
}{
"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 (training data). Each row is a sample, each column is a feature.
Example:
[
[1.2, 0.5, 3.1],
[2.1, 1.3, 2.5],
[0.8, 0.9, 4.2]
]
Binary target labels (0 = non-default, 1 = default)
Available options:
0, 1 Example:
[0, 1, 0]
Optional feature matrix for prediction
Example:
[[1.5, 0.7, 3.3]]
⌘I
