Comparing randomized search and grid search for hyperparameter estimation
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Comparing randomized search and grid search for hyperparameter estimation# Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with SGD training. All parameters that influence the learning are searched simultaneously (except for the number of e
The Johnson-Lindenstrauss bound for embedding with random projections — scikit-learn 0.22.2 documentation
Note Click here to download the full example code or to run this example in your browser via Binder The Johnson-Lindenstrauss bound for embedding with random projections¶ The Johnson-Lindenstrauss lemma states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances. print(__doc__) import sys fro
1.5. Stochastic Gradient Descent
1.5. Stochastic Gradient Descent# Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. Even though SGD has been around in the machine learning community for a long time, it has received a considerable amount of attention just recently in th
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OrthogonalMatchingPursuit
OrthogonalMatchingPursuit# class sklearn.linear_model.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, precompute='auto')[source]# Orthogonal Matching Pursuit model (OMP). Read more in the User Guide. Parameters: n_nonzero_coefsint, default=NoneDesired number of non-zero entries in the solution. Ignored if tol is set. When None and tol is also None, this value is ei
Scikit learn 識別器のいろいろ - Qiita
# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt %matplotlib inline from matplotlib.colors import ListedColormap from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from
LabelEncoder
>>> le = LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) [np.str_('amsterdam'), np.str_('paris'), np.str_('tokyo')] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) [np.str_('tokyo'), np.str_('tokyo'), np.str_('paris')] fit(y)[source]# Fit label encoder. Parameters: yarray-like of sh
Pipeline
Pipeline# class sklearn.pipeline.Pipeline(steps, *, transform_input=None, memory=None, verbose=False)[source]# A sequence of data transformers with an optional final predictor. Pipeline allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final predictor for predictive modeling. Intermediate steps of the pipeline must be trans
RBFSampler
RBFSampler# class sklearn.kernel_approximation.RBFSampler(*, gamma=1.0, n_components=100, random_state=None)[source]# Approximate a RBF kernel feature map using random Fourier features. It implements a variant of Random Kitchen Sinks.[1] Read more in the User Guide. Parameters: gamma‘scale’ or float, default=1.0Parameter of RBF kernel: exp(-gamma * x^2). If gamma='scale' is passed then it uses 1 /
ParameterSampler
ParameterSampler# class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None)[source]# Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a
KernelPCA
KernelPCA# class sklearn.decomposition.KernelPCA(n_components=None, *, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, iterated_power='auto', remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None)[source]# Kernel Principal component analysis (KPCA). Non-linear dimensionality reductio
VotingClassifier
VotingClassifier# class sklearn.ensemble.VotingClassifier(estimators, *, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False)[source]# Soft Voting/Majority Rule classifier for unfitted estimators. Read more in the User Guide. Parameters: estimatorslist of (str, estimator) tuplesInvoking the fit method on the VotingClassifier will fit clones of those original estimators
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Welcome to imbalanced-learn documentation!
scikit-learn-contrib
GitHub - scikit-learn-contrib/category_encoders: A library of sklearn compatible categorical variable encoders
scikit learnより 交差検定で学習モデルの精度を評価 - Qiita
f1_score
[Python][Scikit-learn]主成分分析を用いた次元削減、主成分ベクトルを用いた予測と線形回帰による予測の比較 | DevelopersIO![[Python][Scikit-learn]主成分分析を用いた次元削減、主成分ベクトルを用いた予測と線形回帰による予測の比較 | DevelopersIO](https://cdn-ak-scissors.b.st-hatena.com/image/square/ca665bf96288aed2117d971c0062833d563349e6/height=288;version=1;width=512/https%3A%2F%2Fdevio2023-media.developers.io%2Fwp-content%2Fuploads%2F2016%2F02%2Feye-catch-statistics.png)
[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)
scikit-learn を使った機械学習モデルの構築（アイリスの品種分類） - Qiita
