sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None, n_cols=3, grid_resolution=100, percentiles=(0.05, 0.95), n_jobs=1, verbose=0, ax=None, line_kw=None, contour_kw=None, **fig_kw) [source] Partial dependence plots for features. The len(features) plots are arranged in a grid with n_cols columns. Two-way partial dependence plots are plotted as contour plots. Read more in the User Guide. Parameters: gbrt : BaseGradientBoosting A fitt

An example using IsolationForest for anomaly detection. The IsolationForest ?isolates? observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of such random tree

sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8) [source] Make a large circle containing a smaller circle in 2d. A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide. Parameters: n_samples : int, optional (default=100) The total number of points generated. shuffle: bool, optional (default=True) : Whether to shuffle the samples. noise : double or None (default=None) Standard deviatio

sklearn.datasets.make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None) [source] Generate a sparse symmetric definite positive matrix. Read more in the User Guide. Parameters: dim : integer, optional (default=1) The size of the random matrix to generate. alpha : float between 0 and 1, optional (default=0.95) The probability that a coefficient is zero (see notes). Larger values enforce more sparsity. random_state : int, RandomS

Example of LabelPropagation learning a complex internal structure to demonstrate ?manifold learning?. The outer circle should be labeled ?red? and the inner circle ?blue?. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle. print(__doc__) # Authors: Clay Woolam <clay@woolam.org> # Andreas Mueller <amueller@ais.uni-bonn.de> # License: BSD import numpy as np import matplotlib.pyplot as plt from sk

sklearn.metrics.completeness_score(labels_true, labels_pred) [source] Completeness metric of a cluster labeling given a ground truth. A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster. This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won?t change the score value in any way. This metric is not symmetric: switching label_true with label_pred wil

Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient vector, and this is displayed as a function of the regularization parameter. The right plot shows how exact the solution is. This example illustrates how a well defined solution is found by Ridge regression and how regularization affects the coefficients and their values. The plot on the right shows how the difference of the coefficients from the estimator c

class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None) [source] Linear least squares with l2 regularization. This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d

sklearn.cluster.spectral_clustering(affinity, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10, eigen_tol=0.0, assign_labels='kmeans') [source] Apply clustering to a projection to the normalized laplacian. In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non-convex or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance wh

class sklearn.linear_model.RandomizedLogisticRegression(C=1, scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0.25, tol=0.001, fit_intercept=True, verbose=False, normalize=True, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs', memory=Memory(cachedir=None)) [source] Randomized Logistic Regression Randomized Logistic Regression works by subsampling the training data and fitting a L1-penalized LogisticRegression model where the penalty of a random subset of coeffic