In today’s data-driven world, machine learning classification tasks are ubiquitous across various domains. While building and deploying machine learning models can be complex, APIs provide a convenient way to leverage powerful classification capabilities without the need for extensive setup or infrastructure.

This blog post demonstrates how to use the machine learning classification API provided by techtonique.net (the news are in the new the API’s blog) using three different methods:

1. curl – For command-line users and shell scripts
2. Python requests – For Python developers
3. R httr – For R users
4. Excel – For Excel users

We’ll walk through examples using two classic datasets:

• The Iris dataset for multi-class classification
• The Breast Cancer dataset for binary classification

The input files can be found in Techtonique/datasets (the ones with names ending with 2 specify a training set index).

Each example will show how to:

• Make API calls with proper authentication
• Handle the response data

Let’s get started!

Get a token from: https://www.techtonique.net/token.

Then download the classification dataset:

!wget https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/classification/iris_dataset2.csv
!wget https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/classification/breast_cancer_dataset2.csv

--2025-05-26 23:15:42--  https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/classification/iris_dataset2.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3092 (3.0K) [text/plain]
Saving to: ‘iris_dataset2.csv’

iris_dataset2.csv   100%[===================>]   3.02K  --.-KB/s    in 0s      

2025-05-26 23:15:43 (39.9 MB/s) - ‘iris_dataset2.csv’ saved [3092/3092]

--2025-05-26 23:15:43--  https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/classification/breast_cancer_dataset2.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 122538 (120K) [text/plain]
Saving to: ‘breast_cancer_dataset2.csv’

breast_cancer_datas 100%[===================>] 119.67K  --.-KB/s    in 0.01s   

2025-05-26 23:15:43 (8.73 MB/s) - ‘breast_cancer_dataset2.csv’ saved [122538/122538]

1 – curl

!curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@iris_dataset2.csv;type=text/csv" \
"https://www.techtonique.net/mlclassification?base_model=GradientBoostingClassifier&n_hidden_features=5&predict_proba=True"

{"y_true":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2],"y_pred":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,2],"0":{"precision":1.0,"recall":1.0,"f1-score":1.0,"support":15.0},"1":{"precision":0.7894736842105263,"recall":1.0,"f1-score":0.8823529411764706,"support":15.0},"2":{"precision":1.0,"recall":0.7333333333333333,"f1-score":0.8461538461538461,"support":15.0},"accuracy":0.9111111111111111,"macro avg":{"precision":0.9298245614035089,"recall":0.9111111111111111,"f1-score":0.9095022624434389,"support":45.0},"weighted avg":{"precision":0.9298245614035088,"recall":0.9111111111111111,"f1-score":0.9095022624434389,"support":45.0},"proba":[[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.9999960244140262,3.334039683616685e-06,6.415462901466744e-07],[0.9999960244140262,3.334039683616685e-06,6.415462901466744e-07],[0.9999960244140262,3.334039683616685e-06,6.415462901466744e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[0.999996024414054,3.3340396558155455e-06,6.415462901466922e-07],[3.045033869468758e-06,0.9999937901439727,3.164822157743673e-06],[3.4085260162096436e-06,0.9999931695319606,3.4219420233008224e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[9.588330585448842e-06,0.9998587655346274,0.0001316461347872],[3.4085260162096436e-06,0.9999931695319606,3.4219420233008224e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[3.4085260162096436e-06,0.9999931695319606,3.4219420233008224e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[0.004932272272005095,0.9799043377817493,0.015163389946245788],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[9.588330585508172e-06,0.9998587655408142,0.00013164612860042232],[3.4085260162096436e-06,0.9999931695319606,3.4219420233008224e-06],[3.1524138718715695e-06,0.9999936827643102,3.1648218179040277e-06],[8.748137691624919e-07,2.8391363939081826e-06,0.9999962860498369],[3.4506054062196775e-05,0.007887348624295493,0.9920781453216423],[8.620706075486918e-07,2.5556823801935716e-06,0.9999965822470123],[0.007316994751226876,0.9353406793501166,0.05734232589865652],[0.006129685125947995,0.7712671846855687,0.22260313018848327],[0.003273968018685709,0.969594174153424,0.027131857827890307],[7.169724003427306e-05,0.004745498561275488,0.9951828041986902],[2.245977897875399e-06,8.537222217088907e-06,0.9999892167998851],[0.00016867136329469363,0.568295956512619,0.4315353721240864],[3.1992229758050182e-06,0.0008659343830747356,0.9991308663939493],[2.301715170556224e-05,0.005261230377511008,0.9947157524707835],[8.748139925370251e-07,2.5837975927175516e-06,0.9999965413884147],[8.748137418487531e-07,2.8703586517843243e-06,0.9999962548276062],[2.2462428700241693e-06,1.3402409372558758e-05,0.9999843513477573],[8.748139925370251e-07,2.5837975927175516e-06,0.9999965413884147]]}

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/iris_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/mlclassification?base_model=RandomForestClassifier&n_hidden_features=5&predict_proba=False"

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/iris_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/mlclassification?base_model=RandomForestClassifier&n_hidden_features=5&predict_proba=True"

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/breast_cancer_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/mlclassification?base_model=RandomForestClassifier&n_hidden_features=5&predict_proba=True"

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/breast_cancer_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/gbdtclassification" # default is lightgbm

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/breast_cancer_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/gbdtclassification?model_type=xgboost"

Use https://curlconverter.com/ to translate this request in your favorite programming language.

curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@/Users/t/Documents/datasets/tabular/classification/breast_cancer_dataset2.csv;type=text/csv" \
"http://127.0.0.1:8000/gbdtclassification?model_type=xgboost&predict_proba=False"

Use https://curlconverter.com/ to translate this request in your favorite programming language.

2 – Python

import requests

headers = {
    'Authorization': 'Bearer YOUR_TOKEN_HERE',
}

params = {
    'base_model': 'RandomForestClassifier',
    'n_hidden_features': '5',
    'predict_proba': 'True',
}

files = {
    'file': ('breast_cancer_dataset2.csv', open('breast_cancer_dataset2.csv', 'rb'), 'text/csv'),
}

response = requests.post('https://www.techtonique.net/mlclassification',
                         params=params, headers=headers,
                         files=files)

import matplotlib.pyplot as plt
from sklearn.calibration import calibration_curve
def plot_calibration_curve(y_true, y_prob, n_bins=10, title='Calibration Plot'):
    """Plot calibration curve for a single class."""
    prob_true, prob_pred = calibration_curve(y_true, y_prob, n_bins=n_bins)

    plt.figure(figsize=(8, 6))
    plt.plot(prob_pred, prob_true, 's-', label='Calibration curve')
    plt.plot([0, 1], [0, 1], '--', label='Perfectly calibrated')
    plt.xlabel('Mean predicted probability')
    plt.ylabel('True probability')
    plt.title(title)
    plt.legend()
    plt.grid(True)
    plt.show()


response_json = response.json()
y_true = response_json['y_true']
y_prob = [response_json['proba'][i][1] for i in range(len(y_true))]
plot_calibration_curve(y_true, y_prob)

3 – R

# prompt: load rpy2 extension

%load_ext rpy2.ipython

%%R

library(httr)

headers = c(
  Authorization = "Bearer YOUR_TOKEN_HERE"
)

params = list(
  base_model = "GradientBoostingClassifier",
  n_hidden_features = "5",
  predict_proba = "True"
)

# file from:
files = list(
  file = upload_file("iris_dataset2.csv")
)

res <- httr::POST(url = "https://www.techtonique.net/mlclassification", httr::add_headers(.headers=headers), query = params, body = files, encode = "multipart")

print(res)

Response [https://www.techtonique.net/mlclassification?base_model=GradientBoostingClassifier&n_hidden_features=5&predict_proba=True]
  Date: 2025-05-26 23:16
  Status: 200
  Content-Type: application/json
  Size: 3.67 kB

4 - Excel

For more ways to interact, e.g using Excel:

• https://thierrymoudiki.github.io/blog/2025/03/28/python/xlwings-lite-techtonique
• https://thierrymoudiki.github.io/blog/2024/11/03/python/r/techtonique/scenario-simulations-techtonique
• https://thierrymoudiki.github.io/blog/2024/09/30/python/vba-web-forecasting