loss = tf.reduce_mean(tf.losses.mean_absolute_percentage_error(label, predict)) return loss 1. 2. 3. 4. 5. MSLE 均方对数误差 (mean_squared_logarithmic_error) # Mean Squared Logarithmic Error (MSLE) def getMsleLoss(predict, label): loss = tf.reduce_mean(tf.losses.mean_squared_logarit...
Mean Squared Error (MSE)Root Mean Squared Logarithmic Error (RMSLE)R^2 Score 模型选择 所呈现的问题的特点是: 回归:目标变量是一个连续型数值。 数据集小:小于100K的样本量。 少数特征应该是重要的:相关矩阵表明少数特征包含预测目标变量的信息。 这些特点给予了岭回归、支持向量回归、集成回归、随机森林回归等...
R2值越接近1,表示模型的解释能力越强,预测效果越好。 RMSLE %28Root Mean Squared Logarithmic Error%29:均方根对数误差,适用于对数变换后的数据,或者当误差的对数变换后更接近正态分布时使用。 MAPE %28Mean Absolute Percentage Error%29:平均绝对百分比误差,是预测误差与实际值之间差的绝对值占实际值的百分比的平...
1.2、mean_absolute_error:平均绝对误差,缩写MAE。平均绝对误差是所有单个观测值与算术平均值的偏差的绝对值的平均。 1.3、mean_absolute_percentage_error译为平均绝对百分比误差 ,缩写MAPE。 1.4、mean_squared_logarithmic_error译为均方对数误差,缩写MSLE。 1.5、 squared_hinge $ sh = \frac{1}{m}\sum_{i}m...
The stacked regressors used are XGBoost, LightGBM, the Root Mid-Mean Squared Logarithmic Error (RMSLE) function for the purposes of compute the relation between the values predictable for the auto-learning model given by this model and the actual target value, that is, the mpg of the automatic...
Python 是一种功能强大、灵活且易于学习的编程语言。它是许多专业人士、爱好者和科学家的首选编程语言。Python 的强大之处来自其庞大的软件包生态系统和友好的社区,以及其与编译扩展模块无缝通信的能力。这意味着 Python 非常适合解决各种问题,特别是数学问题。
This example shows you how to calculate the square root, logarithmic value, and exponential value of a complex number. You can read the documentation if you want to learn more about the cmath module.NumPy vs math Several notable Python libraries can be used for mathematical calculations. One ...
We should also define the loss function, Mean Squared Error, which will be minimized by our algorithms: def mean_squared_error(y_true, y_pred): """ MSE as a loss function. It is defined as: Loss = (1/n) * Σ((y - f(x))²), where: - n: the length of the dataset - y...
`tf.contrib.losses.mean_pairwise_squared_error` | | `tf.contrib.losses.mean_squared_error` | | `tf.contrib.losses.sigmoid_cross_entropy` | | `tf.contrib.losses.softmax_cross_entropy` | | `tf.contrib.losses.sparse_softmax_cross_entropy` | | `tf.contrib.losses.log(predictions,labels,...
mean_squared_logarithmic_error或msle squared_hinge hinge binary_crossentropy(亦称作对数损失, logloss) categorical_crossentropy:亦称作多类的对数损失,注意使用该目标函数时,需要将标签转化为形如 (nb_samples, nb_classes) 的二值序列 sparse_categorical_crossentrop:如上,但接受稀疏标签。注意,使用该函数时仍然...