Example of Precision-Recall metric to evaluate classifier output quality. In information retrieval, precision is a measure of result relevancy, while recall is a measure of how many truly relevant results are returned. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. High scores for both show that the classifier is returning accurate results (high precisio

This example demonstrates how to approximate a function with a polynomial of degree n_degree by using ridge regression. Concretely, from n_samples 1d points, it suffices to build the Vandermonde matrix, which is n_samples x n_degree+1 and has the following form: [[1, x_1, x_1 ** 2, x_1 ** 3, ...], [1, x_2, x_2 ** 2, x_2 ** 3, ...], ...] Intuitively, this matrix can be interpreted as a matrix of pseudo features (the points raised to some power). The matrix is akin to (but different from) the

In this plot you can see the training scores and validation scores of an SVM for different values of the kernel parameter gamma. For very low values of gamma, you can see that both the training score and the validation score are low. This is called underfitting. Medium values of gamma will result in high values for both scores, i.e. the classifier is performing fairly well. If gamma is too high, the classifier will overfit, which means that the training score is good but the validation score i

On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset. Note that the training score and the cross-validation score are both not very good at the end. However, the shape of the curve can be found in more complex datasets very often: the training score is very high at the beginning and decreases and the cross-validation score is very low at the beginning and increases. On the right side we see the learning curve of an SVM with RBF kernel. We can see clea

This example shows how to use cross_val_predict to visualize prediction errors. from sklearn import datasets from sklearn.model_selection import cross_val_predict from sklearn import linear_model import matplotlib.pyplot as plt lr = linear_model.LinearRegression() boston = datasets.load_boston() y = boston.target # cross_val_predict returns an array of the same size as `y` where each entry # is a prediction obtained by cross validation: predicted = cross_val_predict(lr, boston.data, y, c

Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset. This plot compares the decision surfaces learned by a decision tree classifier (first column), by a random forest classifier (second column), by an extra- trees classifier (third column) and by an AdaBoost classifier (fourth column). In the first row, the classifiers are built using the sepal width and the sepal length features only, on the second row using the petal length and sepal len

Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. See decision tree for more information on the estimator. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.tree import DecisionTreeClassifier # Parameters n_classes

Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three exemplary classifiers are initialized (DecisionTreeClassifier, KNeighborsClassifier, and SVC) and used to initialize a soft-voting VotingClassifier with weights [2, 1, 2], which means that the predicted probabilities of the DecisionTreeClassifier and

Shows the effect of collinearity in the coefficients of an estimator. Ridge Regression is the estimator used in this example. Each color represents a different feature of the coefficient vector, and this is displayed as a function of the regularization parameter. This example also shows the usefulness of applying Ridge regression to highly ill-conditioned matrices. For such matrices, a slight change in the target variable can cause huge variances in the calculated weights. In such cases, it is

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