For a little idea of machine learning :( stressed: just personal thoughts, if you think it is wrong, you can not adopt, do not spray)
1. The principle is to see, review the math to see how to apply it?
I think, first find the "routine" ( https://www.cnblogs.com/meiriyixiaobu/p/11125995.html ), going to find examples, focusing on complete code, then look at the code, for which the point ( use the parameters of the method of the class meaning, can be seen in the source code description), apply routine, to see how to do basically three steps you can understand that, at the same time, can go to think, why assume What hypothetical premise, then the principle can go to look up this time, for this basic cognitive optimization be there, take a look at an example:
Cross-validation:
1 from sklearn.model_selection import cross_val_score 2 3 scores = cross_val_score(tree_reg, housing_prepared,housing_labels,scoring="neg_mean_squared_error", cv=10) 4 tree_rmse_scores = np.sqrt(-scores)
That we can look at, what is the point cross_val_score this approach is that the following is a source code
"""Evaluate a score by cross-validation Read more in the :ref:`User Guide <cross_validation>`. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like The data to fit. Can be for example a list, or an array. y : array-like, optional, default: None The target variable to try to predict in the case of supervised learning. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. scoring : string, callable or None, optional, default: None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)`` which should return only a single value. Similar to :func:`cross_validate` but only a single metric is permitted. If None, the estimator's default scorer (if available) is used. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.20 ``cv`` default value if None will change from 3-fold to 5-fold in v0.22. n_jobs : int or None, optional (default=None) The number of CPUs to use to do the computation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. verbose : integer, optional The verbosity level. fit_params : dict, optional Parameters to pass to the fit method of the estimator. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs' error_score : 'raise' | 'raise-deprecating' or numeric Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If set to 'raise-deprecating', a FutureWarning is printed before the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Default is 'raise-deprecating' but from version 0.22 it will change to np.nan. Returns ------- scores : array of float, shape=(len(list(cv)),) Array of scores of the estimator for each run of the cross validation. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_score >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> print(cross_val_score(lasso, X, y, cv=3)) # doctest: +ELLIPSIS [0.33150734 0.08022311 0.03531764] See Also --------- :func:`sklearn.model_selection.cross_validate`: To run cross-validation on multiple metrics and also to return train scores, fit times and score times. :func:`sklearn.model_selection.cross_val_predict`: Get predictions from each split of cross-validation for diagnostic purposes. :func:`sklearn.metrics.make_scorer`: Make a scorer from a performance metric or loss function. """
I made it very clear, how to use, how to use, there are also examples, perfect ah, then the rest of what is the principle ah, why cross-validation, there are several cross-validation, which are to be Google!
Just as a piece of advice, try!