Notebooks Gallery

Notebooks Coverage

Exploration

Notebooks about experimentations.

• Search images with deep learning (keras)
• Search images with deep learning (torch)

Search images with deep learning (keras)

Search images with deep learning (torch)

Extensions to scikit-learn

The following notebooks shows machine learning object which follow scikit-learn API not implemented in scikit-learn.

• Decision Tree and Logistic Regression
• Faster Polynomial Features
• KMeans with norm L1
• LogisticRegression and Clustering
• Piecewise classification with scikit-learn predictors
• Piecewise linear regression with scikit-learn predictors
• Predictable t-SNE
• Quantile MLPRegressor
• Quantile Regression
• Regression with confidence interval
• Traceable n-grams with tf-idf
• Transformed Target
• Visualize a scikit-learn pipeline

Decision Tree and Logistic Regression

Faster Polynomial Features

KMeans with norm L1

LogisticRegression and Clustering

Piecewise classification with scikit-learn predictors

Piecewise linear regression with scikit-learn predictors

Predictable t-SNE

Quantile MLPRegressor

Quantile Regression

Regression with confidence interval

Traceable n-grams with tf-idf

Transformed Target

Visualize a scikit-learn pipeline

Extensions to scikit-learn involving Cython

Experiments with scikit-learn and cython. The first experiment implements a criterion for a sklearn.tree.DecisionTreeRegressor. This code is based on the API in Criterion which changed in version 0.21.

• Custom DecisionTreeRegressor adapted to a linear regression

Custom DecisionTreeRegressor adapted to a linear regression

Games with (scikit-learn) trees

The notebooks explore trees, mostly trees from scikit-learn, and compute unusual results from the structure.

• Close leaves in a decision trees

Close leaves in a decision trees