bin_edges_:ndarray of ndarray of shape (n_features,) 每个bin 的边。包含不同形状的数组(n_bins_, ) 忽略的特征将有空数组。 n_bins_:ndarray 形状 (n_features,), dtype=np.int_ 每个特征的 bin 数量。宽度太小(即 <= 1e-8)的 bin 将被删除并发出警告。 n_features_in_:int 拟合期间看到的...
for strategy in strategies: enc = KBinsDiscretizer(n_bins=4, encode='ordinal', strategy=strategy) enc.fit(X) grid_encoded = enc.transform(grid) ax = plt.subplot(len(X_list), len(strategies) + 1, i) # 水平条纹 horizontal = grid_encoded[:, 0].reshape(xx.shape) ax.contourf(xx, yy...
deftest_nonuniform_strategies(strategy, expected_2bins, expected_3bins):X = np.array([0,1,2,3,9,10]).reshape(-1,1)# with 2 binsest =KBinsDiscretizer(n_bins=2, strategy=strategy, encode='ordinal') Xt = est.fit_transform(X) assert_array_equal(expected_2bins, Xt.ravel())# with ...
在使用KBinsDiscretizer()函数对标签进行分类时,下列此行代码的正确含义为‘splitter = KBinsDiscretizer(n__bins=4, encode=’ordinal’, strategy=’quantile’)’A.将标签分成4类,几类标签的数量以等差数列的方式排列,标签会分别打成标签分别会打成1,2,3,4。B.将
importnumpyasnpfromsklearn.preprocessingimportKBinsDiscretizerkb=KBinsDiscretizer(n_bins=3,encode="ordinal",strategy="quantile")a=np.array([0,0,0,0,0,0,0,0,1,2]).reshape((-1,1))kb.fit(a).transform(a) /home/david/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_discretizati...
KBinsDiscretizer(strategy, smoothing, n_bins=None, bin_size=None, n_sd=None) Bin continuous data into number of intervals and perform local smoothing. Note Note that the data type of the output value is the same as that of the input value. Therefore, if ...
这道题类似于10进制转化2进制，通过辗转相除求出各个位置上的数字。但这里有个坑，2进制时它是满2进...
enc = KBinsDiscretizer(n_bins=4, encode='ordinal', strategy=strategy) enc.fit(X) grid_encoded = enc.transform(grid) ax = plt.subplot(len(X_list), len(strategies) + 1, i) # 水平条纹 horizontal = grid_encoded[:, 0].reshape(xx.shape) ...
n_bins:int 或array-like,形状(n_features,)(默认值=5) 要生成的 bin 数量。如果 n_bins < 2 则引发 ValueError 。 encode:{‘onehot’,'onehot-dense',‘ordinal’},(默认='onehot') 用于对转换结果进行编码的方法。 一热 使用one-hot 编码对转换后的结果进行编码,并返回一个稀疏矩阵。忽略的特征总...
示例3: test_fit_transform_n_bins_array ▲点赞 4▼ # 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]# 或者: from sklearn.preprocessing.KBinsDiscretizer importtransform[as 别名]deftest_fit_transform_n_bins_array(strategy, expected):est = KBinsDiscretizer(n_bins=[2,...