对于机器学习的一点想法：（强调：仅仅是个人的想法，如果您觉得不对，不采纳即可，勿喷）
1.是先去看原理，复习数学在来看如何应用吗？
我觉得，先找到“套路”（https://www.cnblogs.com/meiriyixiaobu/p/11125995.html），再去找个例子，重点是完整的代码，然后去看代码，对于其中的点（类的方法的用途，参数的意思，均可在源代码中的说明中看到），套用套路，去看如何去做的，基本上就可理解那三步，同时，可以去思考，为什么假设，假设的前提是什么，那么原理就可以去查查，这个时候，对于这个算法的基本认知算是有了，拿一个例子来看：
交叉验证：
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)
那个我们可以看看，cross_val_score这个方法的意义是什么，如下是源代码中的说明
"""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. """
说的很明白，怎么用，如何用，例子也有，完美啊，那么剩下的是什么，原理啊，为什么要交叉验证，交叉验证有几种，这些均可以Google到！
仅仅以此作为一点建议，不妨试试！