Need better error when n_components is float (was: TypeError when fitting GridSearchCV) · Issue #10034 · scikit-learn/scikit-learn · GitHub
import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, accuracy_score from sklearn.calibration import CalibratedClassifierCV from sklearn import model_selection from sklearn.metrics import make_scorer from sklearn.decomposition import PCA, FastICA from sklearn.pipeline import Pipeline feature_len = 36 clf = Rand
Discretizer · Issue #5778 · scikit-learn/scikit-learn
Binarizer transforms continuous values to two states (0 or 1). It would be nice to generalize this to an arbitrary number of states K. This preprocessor would produce a scipy sparse matrix of shape (n_samples, K * n_features) using the one-of-K encoding. The K thresholds could be chosen uniformly between the min and max of each feature or using the K-quantiles. For example, using uniformly chosen
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GitHub - drivendataorg/box-plots-sklearn: An implementation of some of the tools used by the winner of the box plots competition using scikit-learn.
scikit-learn/logistic.py at master · scikit-learn/scikit-learn · GitHub
scikit-learn/base.py at f0ab589f1541b1ca4570177d93fd7979613497e3 · scikit-learn/scikit-learn
GitHub - scikit-learn-contrib/sklearn-pandas: Pandas integration with sklearn
category_encoders/category_encoders/hashing.py at master · scikit-learn-contrib/category_encoders
[MRG+1] label binarizer not used consistently in CalibratedClassifierCV by srivatsan-ramesh · Pull Request #7799 · scikit-learn/scikit-learn![[MRG+1] label binarizer not used consistently in CalibratedClassifierCV by srivatsan-ramesh · Pull Request #7799 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/54090a3dbc9b4364413ac07a6c3c01a2d6482be5/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F8761371fa1e10d4a8089a1589045420974ab25880069cb4a5df0c92ab515956a%2Fscikit-learn%2Fscikit-learn%2Fpull%2F7799)
scikit-learn/pca.py at master · scikit-learn/scikit-learn · GitHub
GitHub - scikit-learn-contrib/hdbscan: A high performance implementation of HDBSCAN clustering.
scikit-learn/sklearn/pipeline.py at main · scikit-learn/scikit-learn
scikit-learn-contrib
GitHub - scikit-learn-contrib/category_encoders: A library of sklearn compatible categorical variable encoders
[MRG+1] Fix cross_val_predict behavior for binary classification in decision_function (Fixes #9589) by reiinakano · Pull Request #9593 · scikit-learn/scikit-learn![[MRG+1] Fix cross_val_predict behavior for binary classification in decision_function (Fixes #9589) by reiinakano · Pull Request #9593 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/b59f213f7eff1103ca5102c2869a8e3f9ebcedf5/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2Fbc8437ade9946b0d63f8811912c2a63424508421e102ed28e66f9120b6ee104c%2Fscikit-learn%2Fscikit-learn%2Fpull%2F9593)
GitHub - scikit-learn-contrib/polylearn: A library for factorization machines and polynomial networks for classification and regression in Python.
https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a805efbe4bb06516670a9b8c690992bd7/sklearn/model_selection/_validation.py
GitHub - scikit-learn-contrib/lightning: Large-scale linear classification, regression and ranking in Python
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scikit-learn/sklearn/ensemble/forest.py at 64e553398b3873de3d9d1e67f0cccb20e01824bc · 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![[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)
