Machine learning

Calibration and selection

How to tune a machine learning model and select the best one.

Calibration of a polynomial regression

Calibration of a polynomial regression

Machine learning algorithm selection example

Machine learning algorithm selection example

Classification

How to create a classification model.

Classification API

Classification API

K nearest neighbors classification

K nearest neighbors classification

Random forest classification

Random forest classification

Clustering

How to create a clustering model.

API

API

Gaussian Mixtures

Gaussian Mixtures

K-means

K-means

Dimension reduction

How to reduce the dimension of a high-dimensional variable.

KL-SVD on Burgers equation

KL-SVD on Burgers equation

Mixture of experts with PCA on Burgers dataset

Mixture of experts with PCA on Burgers dataset

PCA on Burgers equation

PCA on Burgers equation

Quality

It is important to evaluate the quality of a machine learning model before using it. GEMSEO proposes numerical measures and visualizations for this purpose.

Cross-validation

Cross-validation

Error from surrogate discipline

Error from surrogate discipline

Leave-one-out

Leave-one-out

MSE for regression models

MSE for regression models

R2 for regression models

R2 for regression models

RMSE for regression models

RMSE for regression models

Regression

How to create a regression model.

API

API

Advanced mixture of experts

Advanced mixture of experts

GP regression

GP regression

Linear regression

Linear regression

Mixture of experts

Mixture of experts

PCE regression

PCE regression

Polynomial regression

Polynomial regression

RBF regression

RBF regression

Random forest regression

Random forest regression

Data transformation

Fitting a model from transformed data rather than raw data can facilitate the training and improve the quality of the machine learning model. Every machine learning model has a transformer argument to set the transformation policy (none by default). In the special case of regression models, the function create_surrogate() and the SurrogateDiscipline class use BaseRegressor.DEFAULT_TRANSFORMER by default, i.e. MinMaxScaler for both inputs and outputs.

Pipeline

Pipeline

Scalers

Scalers

Scaling

Scaling