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  • Tutorial
  • API
  • Examples
  • Examples Gallery
  • Notebooks Gallery
  • Blog Gallery
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  • Search images with deep learning (keras)
  • Search images with deep learning (torch)
  • 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
  • Custom DecisionTreeRegressor adapted to a linear regression
  • Close leaves in a decision trees
  • Notebooks Coverage

Notebooks Gallery#

Notebooks Coverage

  • Exploration

  • Extensions to scikit-learn

  • Extensions to scikit-learn involving Cython

  • Games with (scikit-learn) trees

Exploration#

Notebooks about experimentations.

  • Search images with deep learning (keras)
  • Search images with deep learning (torch)
_images/search_images_keras.thumb.png

Search images with deep learning (keras)#

_images/search_images_torch.thumb.png

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
_images/decision_tree_logreg.thumb.png

Decision Tree and Logistic Regression#

_images/faster_polynomial_features.thumb.png

Faster Polynomial Features#

_images/kmeans_l1.thumb.png

KMeans with norm L1#

_images/logistic_regression_clustering.thumb.png

LogisticRegression and Clustering#

_images/piecewise_classification.thumb.png

Piecewise classification with scikit-learn predictors#

_images/piecewise_linear_regression.thumb.png

Piecewise linear regression with scikit-learn predictors#

_images/predictable_tsne.thumb.png

Predictable t-SNE#

_images/quantile_mlpregression.thumb.png

Quantile MLPRegressor#

_images/quantile_regression.thumb.png

Quantile Regression#

_images/regression_confidence_interval.thumb.png

Regression with confidence interval#

_images/traceable_ngrams_tfidf.thumb.png

Traceable n-grams with tf-idf#

_images/sklearn_transformed_target.thumb.png

Transformed Target#

_images/visualize_pipeline.thumb.png

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
_images/piecewise_linear_regression_criterion.thumb.png

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
_images/leave_neighbors.thumb.png

Close leaves in a decision trees#

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  • Exploration
  • Extensions to scikit-learn
  • Extensions to scikit-learn involving Cython
  • Games with (scikit-learn) trees
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