preprocessing.MinMaxScaler()
时间: 2024-05-28 22:08:49 浏览: 74
The `MinMaxScaler` from the `preprocessing` module in scikit-learn is a data normalization technique that scales features to a specified range, which is typically between 0 and 1. It transforms the data by subtracting the minimum value of the feature and then dividing by the range of the feature. The formula for scaling a feature using `MinMaxScaler` is given by:
scaled_value = (x - min) / (max - min)
Where `x` is the original value of the feature, `min` and `max` are the minimum and maximum values of the feature, respectively.
The `MinMaxScaler` is useful when the range of the input features is not fixed or known in advance, or when the features have very different scales. It is commonly used in machine learning pipelines to prepare data for modeling, especially when using algorithms that are sensitive to the scale of the input features, such as support vector machines (SVMs) and k-nearest neighbors (KNN).