Notes on Parameter Tuning| xgboost.readthedocs.io
The dataset used in this example is The 20 newsgroups text dataset which will be automatically downloaded, cached and reused for the document classification example. In this example, we tune the hy...| scikit-learn
This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data. The p...| scikit-learn
The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. In mathematical notation, if\hat{y} is the predicted val...| scikit-learn
brought to you by the ITS Research team at QMUL| blog.hpc.qmul.ac.uk
Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of the estimator classes. Typical examples include C,...| scikit-learn
There are 3 different APIs for evaluating the quality of a model’s predictions: Estimator score method: Estimators have a score method providing a default evaluation criterion for the problem they ...| scikit-learn
Gallery examples: Feature agglomeration vs. univariate selection Pipeline ANOVA SVM Recursive feature elimination Poisson regression and non-normal loss Permutation Importance vs Random Forest Feat...| scikit-learn
I tried to illustrate the usage of scikit-learn pipelines.| kevinkle.in