Normalize input data
时间: 2023-10-08 18:12:02 浏览: 35
Normalizing input data is a common technique used in machine learning to scale and transform the data so that it has a mean of zero and a standard deviation of one. This can help improve the performance of the algorithm and make it more accurate.
There are several ways to normalize data, including min-max scaling, z-score normalization, and decimal scaling. Min-max scaling involves scaling the data to a range between 0 and 1, while z-score normalization involves transforming the data so that it has a mean of zero and a standard deviation of one. Decimal scaling involves shifting the decimal point of the data values so that they fall within a specified range.
Normalizing input data is especially important when dealing with features that have different scales or units of measurement. By normalizing the data, we can ensure that each feature contributes equally to the model and prevent features with larger scales from dominating the model.