Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.
Contents
1 - Python version
!pip install tisthemachinelearner
import numpy as np
from sklearn.datasets import load_diabetes, load_breast_cancer
from sklearn.model_selection import train_test_split
from tisthemachinelearner import Classifier, Regressor
# Classification
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = Classifier("LogisticRegression", random_state=42)
clf.fit(X_train, y_train)
print(clf.predict(X_test))
print(clf.score(X_test, y_test))
clf = Classifier("RandomForestClassifier", n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
print(clf.predict(X_test))
print(clf.score(X_test, y_test))
# Regression
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
reg = Regressor("LinearRegression")
reg.fit(X_train, y_train)
print(reg.predict(X_test))
print(np.sqrt(np.mean((reg.predict(X_test) - y_test) ** 2)))
reg = Regressor("RidgeCV", alphas=[0.01, 0.1, 1, 10])
reg.fit(X_train, y_train)
print(reg.predict(X_test))
print(np.sqrt(np.mean((reg.predict(X_test) - y_test) ** 2)))
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
[1 0 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0
1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0
1 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0
1 0 0]
0.956140350877193
[1 0 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0
1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0
1 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0
1 1 0]
0.9649122807017544
[139.5475584 179.51720835 134.03875572 291.41702925 123.78965872
92.1723465 258.23238899 181.33732057 90.22411311 108.63375858
94.13865744 168.43486358 53.5047888 206.63081659 100.12925869
130.66657085 219.53071499 250.7803234 196.3688346 218.57511815
207.35050182 88.48340941 70.43285917 188.95914235 154.8868162
159.36170122 188.31263363 180.39094033 47.99046561 108.97453871
174.77897633 86.36406656 132.95761215 184.53819483 173.83220911
190.35858492 124.4156176 119.65110656 147.95168682 59.05405241
71.62331856 107.68284704 165.45365458 155.00975931 171.04799096
61.45761356 71.66672581 114.96732206 51.57975523 167.57599528
152.52291955 62.95568515 103.49741722 109.20751489 175.64118426
154.60296242 94.41704366 210.74209145 120.2566205 77.61585399
187.93203995 206.49337474 140.63167076 105.59678023 130.70432536
202.18534537 171.13039501 164.91423047 124.72472569 144.81030894
181.99635452 199.41369642 234.21436188 145.95665512 79.86703276
157.36941275 192.74412541 208.89814032 158.58722555 206.02195855
107.47971675 140.93598906 54.82129332 55.92573195 115.01180018
78.95584188 81.56087285 54.37997256 166.2543518 ]
53.85344583676593
[140.48932729 180.39358466 138.26095011 292.70472351 122.54953663
93.61127853 256.94944065 185.46640503 86.4960167 110.59467587
95.04571587 164.19550268 60.59798796 205.82695673 99.72760443
131.91526636 220.91412088 247.87634694 195.84576355 215.78308828
206.82609175 89.01546302 72.05374047 188.47495433 155.71143723
161.25320029 189.08097216 178.04173865 49.65268248 110.50254797
178.39994134 90.08024148 132.14592247 181.98946205 173.37370782
190.81087767 123.38010922 118.90948131 146.69459204 60.67799313
74.18510938 108.16651262 162.96843997 151.55290246 173.76202246
64.5447612 76.57353392 109.83957197 56.57149752 163.18082268
155.2330795 64.94611225 110.68142707 108.69309211 172.0029122
157.94954707 94.8588743 208.43411608 118.81317959 72.11719648
185.80485787 203.47916991 141.32147862 105.78698586 127.7320836
202.81245148 168.55319265 162.78471685 120.58057123 142.15774259
180.74853766 196.43247773 234.92016137 143.87413715 81.91095295
153.24099082 193.15008313 206.58954277 158.12424491 201.30838954
112.09889377 138.42466927 54.61388245 56.57971753 112.85843725
83.27187052 81.11235009 59.60136702 164.50759424]
53.68696471589718
2 - R version
%load_ext rpy2.ipython
%%R
install.packages("reticulate")
%%R
library(reticulate)
# Importation des bibliothèques Python
np <- import("numpy")
sklearn <- import("sklearn")
datasets <- import("sklearn.datasets")
model_selection <- import("sklearn.model_selection")
tisthemachinelearner <- import("tisthemachinelearner")
# Classification
breast_cancer <- datasets$load_breast_cancer(return_X_y = TRUE)
X <- breast_cancer[[1]]
y <- breast_cancer[[2]]
split <- model_selection$train_test_split(X, y, test_size = 0.2, random_state = 42L)
X_train <- split[[1]]
X_test <- split[[2]]
y_train <- split[[3]]
y_test <- split[[4]]
# Logistic Regression
clf <- tisthemachinelearner$Classifier("LogisticRegression", random_state = 42L)
clf$fit(X_train, y_train)
print(clf$predict(X_test))
print(clf$score(X_test, y_test))
# Random Forest Classifier
clf <- tisthemachinelearner$Classifier("RandomForestClassifier", n_estimators = 100L, random_state = 42L)
clf$fit(X_train, y_train)
print(clf$predict(X_test))
print(clf$score(X_test, y_test))
# Regression
diabetes <- datasets$load_diabetes(return_X_y = TRUE)
X <- diabetes[[1]]
y <- diabetes[[2]]
split <- model_selection$train_test_split(X, y, test_size = 0.2, random_state = 42L)
X_train <- split[[1]]
X_test <- split[[2]]
y_train <- split[[3]]
y_test <- split[[4]]
# Linear Regression
reg <- tisthemachinelearner$Regressor("LinearRegression")
reg$fit(X_train, y_train)
y_pred <- reg$predict(X_test)
print(y_pred)
print(np$sqrt(np$mean((y_pred - y_test) ** 2)))
# Ridge Regression with Cross-Validation
reg <- tisthemachinelearner$Regressor("RidgeCV", alphas = c(0.01, 0.1, 1, 10))
reg$fit(X_train, y_train)
y_pred_ridge <- reg$predict(X_test)
print(y_pred_ridge)
print(np$sqrt(np$mean((y_pred_ridge - y_test) ** 2)))
[1] 1 0 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0
[38] 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0
[75] 1 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
0 1 0 0 1 1 1 0 1 1 0
[112] 1 0 0
[1] 0.9561404
[1] 1 0 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0
[38] 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0
[75] 1 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0
[112] 1 1 0
[1] 0.9649123
[1] 139.54756 179.51721 134.03876 291.41703 123.78966 92.17235 258.23239
[8] 181.33732 90.22411 108.63376 94.13866 168.43486 53.50479 206.63082
[15] 100.12926 130.66657 219.53071 250.78032 196.36883 218.57512 207.35050
[22] 88.48341 70.43286 188.95914 154.88682 159.36170 188.31263 180.39094
[29] 47.99047 108.97454 174.77898 86.36407 132.95761 184.53819 173.83221
[36] 190.35858 124.41562 119.65111 147.95169 59.05405 71.62332 107.68285
[43] 165.45365 155.00976 171.04799 61.45761 71.66673 114.96732 51.57976
[50] 167.57600 152.52292 62.95569 103.49742 109.20751 175.64118 154.60296
[57] 94.41704 210.74209 120.25662 77.61585 187.93204 206.49337 140.63167
[64] 105.59678 130.70433 202.18535 171.13040 164.91423 124.72473 144.81031
[71] 181.99635 199.41370 234.21436 145.95666 79.86703 157.36941 192.74413
[78] 208.89814 158.58723 206.02196 107.47972 140.93599 54.82129 55.92573
[85] 115.01180 78.95584 81.56087 54.37997 166.25435
[1] 53.85345
[1] 140.48933 180.39358 138.26095 292.70472 122.54954 93.61128 256.94944
[8] 185.46641 86.49602 110.59468 95.04572 164.19550 60.59799 205.82696
[15] 99.72760 131.91527 220.91412 247.87635 195.84576 215.78309 206.82609
[22] 89.01546 72.05374 188.47495 155.71144 161.25320 189.08097 178.04174
[29] 49.65268 110.50255 178.39994 90.08024 132.14592 181.98946 173.37371
[36] 190.81088 123.38011 118.90948 146.69459 60.67799 74.18511 108.16651
[43] 162.96844 151.55290 173.76202 64.54476 76.57353 109.83957 56.57150
[50] 163.18082 155.23308 64.94611 110.68143 108.69309 172.00291 157.94955
[57] 94.85887 208.43412 118.81318 72.11720 185.80486 203.47917 141.32148
[64] 105.78699 127.73208 202.81245 168.55319 162.78472 120.58057 142.15774
[71] 180.74854 196.43248 234.92016 143.87414 81.91095 153.24099 193.15008
[78] 206.58954 158.12424 201.30839 112.09889 138.42467 54.61388 56.57972
[85] 112.85844 83.27187 81.11235 59.60137 164.50759
[1] 53.68696
%%R
plot(y_pred_ridge, y_test, type="p", pch=19)
points(y_pred, y_test, col="blue", pch=19)
abline(a = 0, b = 1, col="red")
Citation
For attribution, please cite this work as:
T. Moudiki (2025-02-17). tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R). Retrieved from https://thierrymoudiki.github.io/blog/2025/02/17/python/r/tisthemllearner
BibTeX citation (remove empty spaces)
@misc{ tmoudiki20250217,
author = { T. Moudiki },
title = { tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) },
url = { https://thierrymoudiki.github.io/blog/2025/02/17/python/r/tisthemllearner },
year = { 2025 } }
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- A classifier that's very accurate (and deep) Nov 12, 2023
- mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
- Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
- A diffusion model: G2++ Oct 9, 2023
- Diffusion models in ESGtoolkit + announcements Oct 2, 2023
- An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
- (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
- Forecasting in Python with ahead Sep 11, 2023
- Risk-neutralize simulations Sep 4, 2023
- Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
- Reminder Apr 30, 2023
- Did you ask ChatGPT about who you are? Apr 16, 2023
- A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
- Simple interfaces to the forecasting API Nov 23, 2022
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
- Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
- Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
- Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
- A Machine Learning workflow using Techtonique Jun 6, 2022
- Super Mario Bros © in the browser using PyScript May 8, 2022
- News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
- Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
- New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
- A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
- Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
- Forecasting with `ahead` (Python version) Dec 13, 2021
- Tuning and interpreting LSBoost Nov 15, 2021
- Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
- Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
- Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
- Forecasting with `ahead` Oct 15, 2021
- Classification using linear regression Sep 26, 2021
- `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
- parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
- `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
- Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
- Hyperparameters tuning with GPopt Jun 11, 2021
- A forecasting tool (API) with examples in curl, R, Python May 28, 2021
- Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
- Bayesian Optimization with GPopt Apr 16, 2021
- Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
- Explaining xgboost predictions with the teller Mar 12, 2021
- An infinity of time series models in nnetsauce Mar 6, 2021
- New activation functions in mlsauce's LSBoost Feb 12, 2021
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
- A deeper learning architecture in nnetsauce Dec 18, 2020
- Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
- Bayesian forecasting for uni/multivariate time series Dec 4, 2020
- Generalized nonlinear models in nnetsauce Nov 28, 2020
- Boosting nonlinear penalized least squares Nov 21, 2020
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
- NEWS Oct 30, 2020
- A glimpse into my PhD journey Oct 23, 2020
- Submitting R package to CRAN Oct 16, 2020
- Simulation of dependent variables in ESGtoolkit Oct 9, 2020
- Forecasting lung disease progression Oct 2, 2020
- New nnetsauce Sep 25, 2020
- Technical documentation Sep 18, 2020
- A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
- Back next week, and a few announcements Sep 4, 2020
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
- nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
- Maximizing your tip as a waiter (Part 2) Jul 10, 2020
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
- Announcements Jun 26, 2020
- Parallel AdaOpt classification Jun 19, 2020
- Comments section and other news Jun 12, 2020
- Maximizing your tip as a waiter Jun 5, 2020
- AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
- AdaOpt May 15, 2020
- Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
- Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
- Grid search cross-validation using crossval Apr 10, 2020
- Documentation for the querier, a query language for Data Frames Apr 3, 2020
- Time series cross-validation using crossval Mar 27, 2020
- On model specification, identification, degrees of freedom and regularization Mar 20, 2020
- Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
- R notebooks for nnetsauce Mar 6, 2020
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
- Create a specific feed in your Jekyll blog Feb 21, 2020
- Git/Github for contributing to package development Feb 14, 2020
- Feedback forms for contributing Feb 7, 2020
- nnetsauce for R Jan 31, 2020
- A new version of nnetsauce (v0.3.1) Jan 24, 2020
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
- Search bar, new year 2020 Jan 10, 2020
- 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
- Understanding model interactions with the `teller` Dec 13, 2019
- Using the `teller` on a classifier Dec 6, 2019
- Benchmarking the querier's verbs Nov 29, 2019
- Composing the querier's verbs for data wrangling Nov 22, 2019
- Comparing and explaining model predictions with the teller Nov 15, 2019
- Tests for the significance of marginal effects in the teller Nov 8, 2019
- Introducing the teller Nov 1, 2019
- Introducing the querier Oct 25, 2019
- Prediction intervals for nnetsauce models Oct 18, 2019
- Using R in Python for statistical learning/data science Oct 11, 2019
- Model calibration with `crossval` Oct 4, 2019
- Bagging in the nnetsauce Sep 25, 2019
- Adaboost learning with nnetsauce Sep 18, 2019
- Change in blog's presentation Sep 4, 2019
- nnetsauce on Pypi Jun 5, 2019
- More nnetsauce (examples of use) May 9, 2019
- nnetsauce Mar 13, 2019
- crossval Mar 13, 2019
- test Mar 10, 2019
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