PowerTransformer
PowerTransformer# class sklearn.preprocessing.PowerTransformer(method='yeo-johnson', *, standardize=True, copy=True)[source]# Apply a power transform featurewise to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant varia
RobustScaler
RobustScaler# class sklearn.preprocessing.RobustScaler(*, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False)[source]# Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (2
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
13. Choosing the right estimator
13. Choosing the right estimator# Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any est
API Reference — scikit-learn 0.19.2 documentation
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3.2.4.3.4. sklearn.ensemble.ExtraTreesRegressor — scikit-learn 0.20.1 documentation
ExtraTreesRegressor# class sklearn.ensemble.ExtraTreesRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monoton
7.1. Pipelines and composite estimators
7.1. Pipelines and composite estimators# To build a composite estimator, transformers are usually combined with other transformers or with predictors (such as classifiers or regressors). The most common tool used for composing estimators is a Pipeline. Pipelines require all steps except the last to be a transformer. The last step can be anything, a transformer, a predictor, or a clustering estimat
「実践力を身につけるPythonの教科書」の「Scikit learn」でエラーが出たので対処した話｜てくてくぷれいす
環境macOS Mojave(10.14) pyenv Python3.6.7 scikit-image 0.14.1 scikit-learn 0.20.0 scipy 1.1.0 スポンサーリンク impをimportlibへ書き換えるまずはエラーの全文を見てみましょう． /Users/USERNAME/.pyenv/versions/3.6.7/lib/python3.6/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses impo
Plotting Learning Curves and Checking Models’ Scalability
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Plotting Learning Curves and Checking Models’ Scalability# In this example, we show how to use the class LearningCurveDisplay to easily plot learning curves. In addition, we give an interpretation to the learning curves obtained for a naive Bayes and SVM classifiers. Then, we expl
SVC
SVC# class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)[source]# C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the num
Visualizing cross-validation behavior in scikit-learn
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Visualizing cross-validation behavior in scikit-learn# Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in order to avoid model overfitting, to standardize the number of groups i
1.6. Nearest Neighbors — scikit-learn 0.20.0 documentation
1.6. Nearest Neighbors# sklearn.neighbors provides functionality for unsupervised and supervised neighbors-based learning methods. Unsupervised nearest neighbors is the foundation of many other learning methods, notably manifold learning and spectral clustering. Supervised neighbors-based learning comes in two flavors: classification for data with discrete labels, and regression for data with cont
sklearn.model_selection.ShuffleSplit — scikit-learn 0.20.0 documentation
ShuffleSplit# class sklearn.model_selection.ShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]# Random permutation cross-validator. Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlappin
定番のPython機械学習ライブラリ「scikit-learn」ではじめての学習モデル作成から改善まで (1/3)：CodeZine（コードジン）
本連載ではプログラミングの基本は理解しているが、より実践的なデータ解析に取り組みたい方を対象に、スクリプト言語によるデータ解析の実践を解説します。スクリプト言語の中でも特にデータ解析環境が整っているPythonをとりあげ、対話型解析ツールやライブラリによるデータ解析の実行・可視化の方法をを解説します。第4回となる本稿ではPythonによる機械学習を解説します。まず機械学習の概観について確認し、Jupyter Notebookとライブラリscikit-learn使った機械学習の手順を解説します。 対象読者 Pythonの基本的な文法を理解しておりデータ解析のスキルアップに取り組みたい方 サンプルの動作確認環境 MacOS 10.13 Anaconda 5.1 Python 3.6 Jupyter Notebook 5.4 機械学習の概要 本稿では、実際にPythonでの機械学習に入る前に、機
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scikit-learn: machine learning in Python
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
