class sklearn.manifold.TSNE(n_components=2, perplexity=30.0, early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000, n_iter_without_progress=30, min_grad_norm=1e-07, metric='euclidean', init='random', verbose=0, random_state=None, method='barnes_hut', angle=0.5) [source] t-distributed Stochastic Neighbor Embedding. t-SNE [1] is a tool to visualize high-dimensional data. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergen

class sklearn.manifold.SpectralEmbedding(n_components=2, affinity='nearest_neighbors', gamma=None, random_state=None, eigen_solver=None, n_neighbors=None, n_jobs=1) [source] Spectral embedding for non-linear dimensionality reduction. Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point. Read more in the User Guide. Parameters

class sklearn.manifold.LocallyLinearEmbedding(n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=1) [source] Locally Linear Embedding Read more in the User Guide. Parameters: n_neighbors : integer number of neighbors to consider for each point. n_components : integer number of coordinates for the manifold reg : float regularization const

class sklearn.manifold.MDS(n_components=2, metric=True, n_init=4, max_iter=300, verbose=0, eps=0.001, n_jobs=1, random_state=None, dissimilarity='euclidean') [source] Multidimensional scaling Read more in the User Guide. Parameters: metric : boolean, optional, default: True compute metric or nonmetric SMACOF (Scaling by Majorizing a Complicated Function) algorithm n_components : int, optional, default: 2 number of dimension in which to immerse the similarities overridden if initial arra

class sklearn.manifold.Isomap(n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=1) [source] Isomap Embedding Non-linear dimensionality reduction through Isometric Mapping Read more in the User Guide. Parameters: n_neighbors : integer number of neighbors to consider for each point. n_components : integer number of coordinates for the manifold eigen_solver : [?auto?|?arpack?|?dense?] ?auto? : Attempt to choos

An illustration of various embeddings on the digits dataset. The RandomTreesEmbedding, from the sklearn.ensemble module, is not technically a manifold embedding method, as it learn a high-dimensional representation on which we apply a dimensionality reduction method. However, it is often useful to cast a dataset into a representation in which the classes are linearly-separable. t-SNE will be initialized with the embedding that is generated by PCA in this example, which is not the default setti

An application of the different Manifold learning techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to ?spread it open? whilst projecting it onto two dimensions. For a similar example, where the methods are applied to the S-curve dataset, see

Show below is a logistic-regression classifiers decision boundaries on the iris dataset. The datapoints are colored according to their labels. print(__doc__) # Code source: Ga Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. Y = iris.target h

Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve. print(__doc__) # Code source: Gael Varoquaux # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model # this is our test set, it's just a straight line with some # Gaussian noise xmin, xmax = -5, 5 n_samples = 100 np.random.seed(0) X = np.random.normal(size=n_samples) y =

class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=1, verbose=False) [source] Theil-Sen Estimator: robust multivariate regression model. The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and