scikit-learn/sklearn/ensemble/forest.py at 64e553398b3873de3d9d1e67f0cccb20e01824bc · scikit-learn/scikit-learnYou signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
1.7. Gaussian Processes
1.7. Gaussian Processes# Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction interpolates the observations (at least for regular kernels). The prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and decide based on
Comparison of LDA and PCA 2D projection of Iris dataset
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Comparison of LDA and PCA 2D projection of Iris dataset# The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA) applied to this data ident
Examples
Examples# This is the gallery of examples that showcase how scikit-learn can be used. Some examples demonstrate the use of the API in general and some demonstrate specific applications in tutorial form. Also check out our user guide for more detailed illustrations. Release Highlights# These examples illustrate the main features of the releases of scikit-learn.
Pipelining: chaining a PCA and a logistic regression — scikit-learn 0.19.2 documentation
Pipelining: chaining a PCA and a logistic regression¶ The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to set the dimensionality of the PCA print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn i
Contributing
Contributing# This project is a community effort, and everyone is welcome to contribute. It is hosted on scikit-learn/scikit-learn. The decision making process and governance structure of scikit-learn is laid out in Scikit-learn governance and decision-making. Scikit-learn is somewhat selective when it comes to adding new algorithms, and the best way to contribute and to help the project is to sta
StratifiedShuffleSplit
StratifiedShuffleSplit# class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]# Class-wise stratified ShuffleSplit cross-validator. Provides train/test indices to split data in train/test sets. This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds ar
Selecting the number of clusters with silhouette analysis on KMeans clustering
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Selecting the number of clusters with silhouette analysis on KMeans clustering# Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette plot displays a measure of how close each point in one cluster is to points in the neighb
Generalized additive models (GAMs)? · Issue #3482 · scikit-learn/scikit-learn
Hello there, Thanks for making this fantastic library. I use it every day in my bioinformatics research. We're developing a toolkit for single-cell RNA-seq analysis (http://github.com/yeolab/flotilla) and want to add all current state-of-the-art analyses. Unfortunately, most of these are in R. I can reimplemement some of them, but they rely on certain R packages, in particular VGAM, aka Vector Gen
[MRG] Complete rewrite of the tree module by glouppe · Pull Request #2131 · scikit-learn/scikit-learn
Here are some good news for Kaggle competitors... :) In this PR, I propose a complete rewrite of the core tree module (_tree.pyx) and of all tree-dependent estimators. In particular, this new implementation factorizes out the splitting strategy at the core of the construction process of a tree. Such a strategy is now implemented in a Splitter object as specified in the new interface in _tree.pxd.
![[MRG] Complete rewrite of the tree module by glouppe · Pull Request #2131 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/f2323ef505e4f3057d815b86802e66427acff909/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F503a789573605301a3e1e4cc041bfac4b42ef3b46033b90fd899ff97cba34920%2Fscikit-learn%2Fscikit-learn%2Fpull%2F2131)
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
[MRG+1] RandomForest decrease allocated memory when using bootstrap option. (Fix issue#4774) by tokoroten · Pull Request #4783 · scikit-learn/scikit-learn![[MRG+1] RandomForest decrease allocated memory when using bootstrap option. (Fix issue#4774) by tokoroten · Pull Request #4783 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/33319dbb27dc9621729bd70d1b300b879476ebae/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F4011c068282397c2f29f061305076f82c6f19262ae7b3273d87fc9f226fbadaf%2Fscikit-learn%2Fscikit-learn%2Fpull%2F4783)
RandomForest is allocating a lot of memory when using bootstrap option. · Issue #4774 · scikit-learn/scikit-learn
KNeighborsClassifier is missing predict_proba() · Issue #895 · scikit-learn/scikit-learn
Welcome to imbalanced-learn documentation!
scikit-learn/k_means_.py at master · scikit-learn/scikit-learn · GitHub
scikit-learn/sklearn/ensemble/forest.py at a95203b249c1cf392f86d001ad999e29b2392739 · scikit-learn/scikit-learn
[WIP] Decision tree export to javascript by tokoroten · Pull Request #5054 · scikit-learn/scikit-learn![[WIP] Decision tree export to javascript by tokoroten · Pull Request #5054 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/30e533e5ac6d7825f4f21412e87386830cdd2ad0/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F01ea0a057c20334990becc08cc6ce8b441fd2df506145c728cbff59db241ed01%2Fscikit-learn%2Fscikit-learn%2Fpull%2F5054)
[MRG+2] Adding Implementation of SAG - next episode by TomDLT · Pull Request #4738 · scikit-learn/scikit-learn![[MRG+2] Adding Implementation of SAG - next episode by TomDLT · Pull Request #4738 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/6599a0b819005c95941b576c0720978ea5d42e33/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F33e5a7b99a216a9cb77c28d500361a7229e6a44b5997cb4239787d54c8081813%2Fscikit-learn%2Fscikit-learn%2Fpull%2F4738)
scikit-learn/sklearn/tree/tree.py at 77c963d0348437470f5cf1e619e7702aa8c56d1c · scikit-learn/scikit-learn
GitHub - scikit-learn-contrib/scikit-learn-contrib: scikit-learn compatible projects
