1.15. Isotonic regression
1.15. Isotonic regression# The class IsotonicRegression fits a non-decreasing real function to 1-dimensional data. It solves the following problem: subject to \(\hat{y}_i \le \hat{y}_j\) whenever \(X_i \le X_j\), where the weights \(w_i\) are strictly positive, and both X and y are arbitrary real quantities. The increasing parameter changes the constraint to \(\hat{y}_i \ge \hat{y}_j\) whenever \(
Demo of DBSCAN clustering algorithm
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Demo of DBSCAN clustering algorithm# DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regions of high density and expands clusters from them. This algorithm is good for data which contains clusters of similar density. See the Comparing dif
NuSVR
NuSVR# class sklearn.svm.NuSVR(*, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)[source]# Nu Support Vector Regression. Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-S
An introduction to machine learning with scikit-learn
An introduction to machine learning with scikit-learn# Machine learning: the problem setting# In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems f
Pipelining: chaining a PCA and a logistic regression — scikit-learn 0.19.2 documentation
Pipelining: chaining a PCA and a logistic regression¶ The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to set the dimensionality of the PCA print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn i
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