scikit-learn: machine learning in Python — scikit-learn 1.6.1 …
Preprocessing. Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature …
top_k_accuracy_score — scikit-learn 1.6.1 documentation
top_k_accuracy_score# sklearn.metrics. top_k_accuracy_score (y_true, y_score, *, k = 2, normalize = True, sample_weight = None, labels = None) [source] # Top-k Accuracy …
Examples — scikit-learn 1.6.1 documentation
Examples#. This is the gallery of examples that showcase how scikit-learn can be used. Some examples demonstrate the use of the API in general and some demonstrate specific …
OneHotEncoder — scikit-learn 1.6.1 documentation
drop {‘first’, ‘if_binary’} or an array-like of shape (n_features,), default=None. Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where …
normalize — scikit-learn 1.6.1 documentation
normalize# sklearn.preprocessing. normalize (X, norm = 'l2', *, axis = 1, copy = True, return_norm = False) [source] # Scale input vectors individually to unit norm (vector length). Read more in …
accuracy_score — scikit-learn 1.6.1 documentation
accuracy_score# sklearn.metrics. accuracy_score (y_true, y_pred, *, normalize = True, sample_weight = None) [source] # Accuracy classification score. In multilabel classification, …
User Guide — scikit-learn 1.6.1 documentation
Jan 1, 2010 · Supervised learning- Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle …
SVR — scikit-learn 1.6.1 documentation
SVR# class sklearn.svm. SVR (*, kernel = 'rbf', degree = 3, gamma = 'scale', coef0 = 0.0, tol = 0.001, C = 1.0, epsilon = 0.1, shrinking = True, cache_size = 200, verbose = False, max_iter = …
cross_val_score — scikit-learn 1.6.1 documentation
n_jobs int, default=None. Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. None means 1 unless in a …
Support Vector Regression (SVR) using linear and non-linear kernels
Toy example of 1D regression using linear, polynomial and RBF kernels. Generate sample data: Fit regression model: Look at the results: Total running time of the script:(0 minutes 0.409 …