百度试题 结果1 题目代码OneHotEncoder(sparse = False)中的sparse = False表示返回一个稀疏矩阵。相关知识点: 试题来源: 解析 错误 代码OneHotEncoder(sparse = False)中的sparse = False表示不返回稀疏矩阵。反馈 收藏
encoder = OneHotEncoder(sparse_output=False) is a class in thesklearn.preprocessingmodule of thescikit-learnlibrary ¹. It is used to encode categorical features as a one-hot numeric array ¹. The input to this transformer should be an array-like of integers or strings, denoting the valu...
1.基本用例：训练和测试分类器练习2.更高级的用例：在训练和测试分类器之前预处理数据2.1 标准化您的...
如果没有'sparse'参数,找出正确的用法或替代方案: 由于sparse参数在新版本的OneHotEncoder中已被移除,我们需要使用其他方式来控制输出是否稀疏。在scikit-learn的新版本中,可以通过设置sparse参数为True或False来控制fit_transform或transform方法的输出格式。例如: python from sklearn.preprocessing import OneHotEncoder im...
from sklearn.preprocessing import OneHotEncoder tf.logging.set_verbosity(tf.logging.INFO) def cnn_model_fn(features, labels, mode): """Model function for CNN""" #Input Layer input_layer = tf.reshape(features["x"], [-1,320,320,3]) ...
elif (c in self.colCateg) or (data[c].dtype == "object"): # OneHotEncoder object enc[c] = OneHotEncoder(sparse=False, dtype=int, handle_unknown="ignore") enc[c] = OneHotEncoder(sparse_output=False, dtype=int, handle_unknown="ignore") # Fit to observed categories enc[c].fit(da...
In the experimental phase, the proposed sparse autoencoder with swish-PReLU activation model achieved effective performance in intrusion detection in light of false alarm rate, detection rate and classification accuracy. From the experimental result, the proposed model showed the maximum of 4.77% ...
In this case, we assume that `sparse` is False # but the parameter validation will raise an error during `fit`. pass # pragma: no cover return tags def _check_X(X): """Use check_array only when necessary, e.g. on lists and other non-array-likes.""" 1 change: 1 addition & ...
def encode(table, include_class=False): """ Return a tuple of (bool (one hot) ndarray, {col: (variable_index, value_index)} mapping) If the input table is sparse, a list of nonzero column indices per row (LIL rows) is returned instead of the one-hot ndarray. """ X, encoded,...
import * from mmcv.cnn import build_conv_layer CONV_LAYERS._register_module( spnn.Conv3d, "SparseConv3d", force=True ) conv_layer = build_conv_layer( dict(type="SparseConv3d"), IC, OC, kernel_size, stride=stride, padding=padding, bias=False ) # Equivalent to spnn.Conv3d(IC, OC, ...