Need better error when n_components is float (was: TypeError when fitting GridSearchCV) · Issue #10034 · scikit-learn/scikit-learn · GitHub
Description Hello! I'm trying to run some code written a year ago or so, but I seem to run into an error when I run try to fit the data provided to GridSearchCV. I tried running the same data through a randomized parameter optimizer, and got the same error. Changing the entries in ytest to integers instead of strings doesn't seem to helper either. Steps/Code to Reproduce import pandas as pd import
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
GitHub - scikit-learn-contrib/hdbscan: A high performance implementation of HDBSCAN clustering.
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering s
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scikit-learn/logistic.py at master · scikit-learn/scikit-learn · GitHub
scikit-learn/sklearn/linear_model/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)
[MRG] Sparse input support for decision tree and forest by arjoly · Pull Request #3173 · scikit-learn/scikit-learn![[MRG] Sparse input support for decision tree and forest by arjoly · Pull Request #3173 · scikit-learn/scikit-learn](https://cdn-ak-scissors.b.st-hatena.com/image/square/364023e0575955b9bd00f2c69316b931842d4606/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F6e62a3444b750b3ae9b5f475c4778838ef7e05a1025ba59a3de3bdb46391b5c7%2Fscikit-learn%2Fscikit-learn%2Fpull%2F3173)
scikit-learn/pca.py at master · scikit-learn/scikit-learn · GitHub
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.
scikit-learn/sklearn/model_selection/_validation.py at ef5cb84a805efbe4bb06516670a9b8c690992bd7 · scikit-learn/scikit-learn
GitHub - scikit-learn-contrib/lightning: Large-scale linear classification, regression and ranking in Python
Third party projects and code snippets
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)
