詳解sklearn中的make_moons函數

make_moons是函數用來生成數據集，在sklearn.datasets里，具體用法如下：

Parameters:    

n_samples : int, optional (default=100)
    The total number of points generated.
shuffle : bool, optional (default=True)
    Whether to shuffle the samples.
noise : double or None (default=None)
    Standard deviation of Gaussian noise added to the data.
random_state : int, RandomState instance or None (default)
    Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns:    
X : array of shape [n_samples, 2]
    The generated samples.
y : array of shape [n_samples]
    The integer labels (0 or 1) for class membership of each sample.

主要參數作用如下：
n_numbers:生成樣本數量
shuffle:是否打亂，類似於將數據集random一下
noise:默認是false，數據集是否加入高斯噪聲
random_state:生成隨機種子，給定一個int型數據，能夠保證每次生成數據相同。
sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)
for example:
X, y = datasets.make_moons(500, noise=0.5)
參考文獻：
【1】https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
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版權聲明：本文為CSDN博主「禪心001」的原創文章，遵循 CC 4.0 BY-SA 版權協議，轉載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/woai8339/article/details/88628509