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...
▲ MSLE Loss与Predictions的性能图 Root Mean Squared Logarithmic Error (RMSLE) RMSLE 通过应用 log 到实际和预测的值,然后进行相减。当同时考虑小误差和大误差时,RMSLE 可以避免异常值的影响。 ▲ RMSLE Loss与Predictions的性能图 Normalized Root Mean Squared Error (NRMSE) 归一化均方根误差(NRMSE)RMSE 有助...
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...
Root Mean Squared Logarithmic Error (RMSLE)RMSLE= ⎷1NN∑i=1(log(xi)−log(yi))2 R^2 ScoreR2=1−∑ni=1e2i∑ni=1(yi−¯y)2 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...
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 ...
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...
`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,...
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...
Mind that if data are expressed in centimetres, then the variance is in centimetres squared, which is not very intuitive. The standard deviation does not have this drawback. For many reasons, mathematicians find the square root in the definition of sss annoying, though; it is why we will ...
Realized volatility is the square root of realized variance, which is the sum of squared return. Realized volatility is used to calculate the performance of the volatility prediction method. Here is the formula for return volatility:σ ^ = 1 n-1 ∑ n=1 N (r n -μ) 2 where r and μ...