sklearn.preprocessing
时间: 2023-12-01 13:02:19 浏览: 33
The sklearn.preprocessing module provides various functions for preprocessing data before it can be used for machine learning. Some of the functions provided by this module are:
1. StandardScaler: This function scales the data such that it has zero mean and unit variance.
2. MinMaxScaler: This function scales the data to a specified range, usually between 0 and 1.
3. RobustScaler: This function scales the data by removing the median and scaling it to the interquartile range.
4. Normalizer: This function scales each sample to have a unit norm.
5. Binarizer: This function converts numerical features into binary features based on a threshold value.
6. LabelEncoder: This function converts categorical variables into numerical variables by assigning a unique label to each category.
7. OneHotEncoder: This function converts categorical variables into binary variables by creating new columns for each category.
8. PolynomialFeatures: This function creates new features by combining existing features using polynomial functions.
These functions can be used to preprocess data for various machine learning models such as linear regression, logistic regression, support vector machines, etc.