Intro

Is a library that implements many tools for machine learning and advanced data analysis.

Data transform

The Sklearn provides a set of tools for data processing. The following table lists some of these classes.

Area of Preprocessing	Class Name	Description
Scaling & Standardization	StandardScaler	Standardizes features by removing the mean and scaling to unit variance (\(z\)-score).
	MinMaxScaler	Scales features to a given range, typically \([0, 1]\).
	MaxAbsScaler	Scales each feature by its maximum absolute value, typically to the range \([-1, 1]\). Useful for sparse data.
	RobustScaler	Scales features using statistics that are robust to outliers (e.g., median and interquartile range).
Normalization	Normalizer	Normalizes samples individually to have unit norm (e.g., \(l_1\) or \(l_2\) norm).
Missing Values Imputation	SimpleImputer	Imputes missing values using a simple strategy (e.g., mean, median, most frequent, or a constant value).
	KNNImputer	Imputes missing values using the K-Nearest Neighbors approach.
	IterativeImputer	Imputes missing values by modeling each feature with missing values as a function of other features and using that estimate for imputation.
Categorical Encoding	OneHotEncoder	Encodes categorical features as a one-hot or dummy feature matrix.
	OrdinalEncoder	Encodes categorical features into ordinal integers.
Feature Transformation	PolynomialFeatures	Generates polynomial and interaction features (e.g., \(x_1^2, x_1 x_2, x_2^2\)).
	FunctionTransformer	Allows creating a transformer from an arbitrary function.
	QuantileTransformer	Transforms features using quantiles, forcing the data to follow a uniform or normal distribution.
	PowerTransformer	Applies power transforms (e.g., Yeo-Johnson or Box-Cox) to make data more Gaussian-like.
Discretization / Binning	KBinsDiscretizer	Bins continuous data into \(k\) discrete intervals.
Pipeline/Composition	Pipeline	Sequentially applies a list of transformers and a final estimator.
	ColumnTransformer	Applies different transformers to different subsets of features.

For more details, check the Data Transform page.