从零开始-Machine Learning学习笔记(16)-使用scikit-learn实现学习曲线
1.导入工具包
from sklearn.model_selection import learning_curve
2.封装函数
在learning_curve中使用的函数原型是：
learning_curve(estimator, X, y, groups=None,
train_sizes=np.linspace(0.1, 1.0, 5), cv=None, scoring=None,
exploit_incremental_learning=False, n_jobs=1,
pre_dispatch="all", verbose=0, shuffle=False,
random_state=None):
其中各个参数说明如下：
estimator : 所使用的模型或方法
X : array-like, shape (n_samples, n_features)
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression; None for unsupervised learning.
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. (default: np.linspace(0.1, 1.0, 5))
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.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if the estimator is a classifier and
yis either binary or multiclass,sklearn.model_selection.StratifiedKFoldis used. In all other cases,sklearn.model_selection.KFoldis used.Refer User Guide for the various cross-validation strategies that can be used here.
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).exploit_incremental_learning : boolean, optional, default: False
If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
pre_dispatch : integer or string, optional
Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like ‘2*n_jobs’.
verbose : integer, optional
Controls the verbosity: the higher, the more messages.
error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, 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.