Examples

Here are some examples on how to use the models in the library. These examples are meant to showcase each model and method defined in the library. Some utility methods, such as certain metrics, are omitted, as the author believes such examples have little to no practical use. However, most core features and functionalities are demonstrated through these examples.

Each example is designed to illustrate the model’s usage, behavior, and performance in various scenarios. The provided scripts include data preprocessing, model training, evaluation, and visualization where applicable. By following these examples, users can gain a better understanding of how to effectively apply the models to different machine learning tasks.

Probability Calibration

Isotonic regression

Isotonic regression

Calibration of Classification Models

Calibration of Classification Models

Deep learning

Deep learning with Attention

Deep learning with Attention

Recurrent networks for time series analysis

Recurrent networks for time series analysis

Bidirectional recurrent layers

Bidirectional recurrent layers

Regression with neural networks

Regression with neural networks

Kolmogorov-Arnold Networks

Kolmogorov-Arnold Networks

MNIST Image classification

MNIST Image classification

Gaussian Processes

Multidimensional Gaussian Process Regression (GPR)

Multidimensional Gaussian Process Regression (GPR)

Gaussian Process Regressor (GPR)

Gaussian Process Regressor (GPR)

Gaussian Process Classification (GPC)

Gaussian Process Classification (GPC)

Linear Models

Linear Regression with confidence intervals

Linear Regression with confidence intervals

Robust Regression with RANSAC

Robust Regression with RANSAC

Polynomial Surface Regression with Total Least Squares

Polynomial Surface Regression with Total Least Squares

Logistic Regression on the Iris Dataset

Logistic Regression on the Iris Dataset

Locally Weighted Regression on a Sine Function

Locally Weighted Regression on a Sine Function

Regularization Path for Ridge, LASSO, and ElasticNet Regression

Regularization Path for Ridge, LASSO, and ElasticNet Regression

Linear and Regularized Regression Models on Synthetic Data

Linear and Regularized Regression Models on Synthetic Data

Metrics

Logistic Regression on Synthetic Data with ROC Curve and AUC

Logistic Regression on Synthetic Data with ROC Curve and AUC

Naive Bayes

Naive Bayes Classifiers on Iris and Synthetic Datasets

Naive Bayes Classifiers on Iris and Synthetic Datasets

Neighbours

K-Nearest Neighbors (KNN) Classification and Regression

K-Nearest Neighbors (KNN) Classification and Regression

Optimizers

Comparison of Optimization Algorithms on the Rosenbrock Function

Comparison of Optimization Algorithms on the Rosenbrock Function

Reinforcement learning

Deep Q-Learning Agent for CartPole-v1

Deep Q-Learning Agent for CartPole-v1

Support vector machines

Support Vector Regression for 3D Surface Fitting

Support Vector Regression for 3D Surface Fitting

Support Vector Classifier Solver Comparison

Support Vector Classifier Solver Comparison

Trees and boosting machines

Outlier detection using isolation forest

Outlier detection using isolation forest

Decision tree and random forest classifiers

Decision tree and random forest classifiers

Boosting Classifier Comparison

Boosting Classifier Comparison

Regression using tree based models

Regression using tree based models

Unsupervised learning

Comparison of dimensionality reduction algorithms

Comparison of dimensionality reduction algorithms

Classification with discriminant analysis

Classification with discriminant analysis

Comparison of clustering algorithms using silhouette scores

Comparison of clustering algorithms using silhouette scores