sklearn.pipeline.make_pipeline(*steps)

class sklearn.kernel_approximation.Nystroem(kernel='rbf', gamma=None, coef0=1, degree=3, kernel_params=None, n_components=100

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM. This demonstrates

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to

class sklearn.neighbors.KDTree KDTree for fast generalized N-point problems KDTree(X, leaf_size=40, metric=?minkowski

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference in performance between the discrete SAMME [2] boosting algorithm

This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-written digit. In order to utilize an 8x8 figure like this, we?d have to first

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector

An example showing how the scikit-learn can be used to recognize images of hand-written digits. This example is commented in the

Section contents In this section, we introduce the