官术网_书友最值得收藏!

Defining our cost function

For regression tasks, the most common cost functions are Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). I'll be using MAE here. It is defined as follows:

Very simply, MAE is the average unsigned error for all examples in the dataset. It's very similar to RMSE; however, we use the absolute value of the difference between y and instead of the square root of the average squared error:

You might be wondering how MAE differs from the more familiar RMSE. In cases where the error is evenly distributed across the dataset, RMSE and MAE will be equal. In cases where there are very large outliers in a dataset, RMSE will be much larger than MAE. Your choice of cost function should be appropriate to your use case. In regard to interpretability, MAE is more interpretable than RMSE as it's the actual average error.
主站蜘蛛池模板: 弥勒县| 萍乡市| 祁门县| 马公市| 内黄县| 洛川县| 承德市| 合肥市| 马尔康县| 宁晋县| 宁晋县| 本溪| 巴马| 彰化县| 八宿县| 扬中市| 文昌市| 枣阳市| 长乐市| 兴城市| 阿克| 兴山县| 交口县| 瑞丽市| 延津县| 偏关县| 肇庆市| 包头市| 南岸区| 沭阳县| 中超| 康定县| 吉木萨尔县| 沾益县| 浪卡子县| 滦南县| 南昌市| 阳朔县| 曲麻莱县| 巩义市| 日喀则市|