FeatureAgglomeration
FeatureAgglomeration# class sklearn.cluster.FeatureAgglomeration(n_clusters=2, *, metric='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean>, distance_threshold=None, compute_distances=False)[source]# Agglomerate features. Recursively merges pair of clusters of features. Read more in the User Guide. Parameters: n_clustersint or None,
PolynomialFeatures
PolynomialFeatures# class sklearn.preprocessing.PolynomialFeatures(degree=2, *, interaction_only=False, include_bias=True, order='C')[source]# Generate polynomial and interaction features. Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form
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 /
Pipeline
Pipeline# class sklearn.pipeline.Pipeline(steps, *, 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 ‘transforms’, that is, they
Comparing different clustering algorithms on toy datasets
Comparing different clustering algorithms on toy datasets# This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dataset-algorithm pairs has been tuned to produce good clustering results. Some algorithms are more sensitive to parameter values than others. The
Plot multi-class SGD on the iris dataset
Plot multi-class SGD on the iris dataset# Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines. import matplotlib.pyplot as plt import numpy as np from sklearn import datasets from sklearn.inspection import DecisionBoundaryDisplay from sklearn.linear_model import SGDClassifier # imp
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https://scikit-learn.org/stable/modules/ensemble.html
https://scikit-learn.org/stable/modules/sgd.html
https://scikit-learn.org/stable/modules/kernel_approximation.html
Model selection: choosing estimators and their parameters
https://scikit-learn.org/stable/modules/learning_curve.html
OneHotEncoder
https://scikit-learn.org/stable/modules/kernel_ridge.html
Variational Gaussian Mixture Models — scikit-learn 0.19.2 documentation
https://scikit-learn.org/stable/modules/biclustering.html
https://scikit-learn.org/stable/modules/grid_search.html
http://scikit-learn.org/stable/auto_examples/plot_feature_selection.html
https://scikit-learn.org/stable/modules/feature_selection.html
scikit-learn: machine learning in Python