The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...| scikit-learn
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
Gallery examples: Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.4 Release Highlights for scikit-learn 1.1 Release Highlights for scikit-learn 1.0 Release Highlights ...| scikit-learn
Two-dimensional, size-mutable, potentially heterogeneous tabular data.| pandas.pydata.org
Source code: Lib/warnings.py Warning messages are typically issued in situations where it is useful to alert the user of some condition in a program, where that condition (normally) doesn’t warrant...| Python documentation
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...| scikit-learn
One-dimensional ndarray with axis labels (including time series).| pandas.pydata.org
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
Sections#| numpydoc.readthedocs.io
The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.,,,, Built-in Functions,,, A, abs(), aiter(), all(), a...| Python documentation