Machine Learning Library sklearn
Official documentation (data) is divided into different sections:
One of our main speakers User Guide (machine learning algorithms theoretical introduction), API (program implementation):
一、User Guide
https://scikit-learn.org/stable/user_guide.html
| Module |
Explanation |
| Supervised learning supervised learning |
Introduction of supervised learning algorithms |
| Unsupervised learning Unsupervised learning |
Unsupervised learning algorithms introduced |
Model selection and evaluation model selection and evaluation
|
Cross-validation parameter adjustment, the model evaluation, verification curve |
| Inspection checks |
|
| Dataset transformations Data Conversion |
Feature extraction, data preprocessing, missing values, unsupervised dimensionality reduction methods, random projection, nuclear approximation, prediction target conversion |
| Dataset loading utilities data download |
Toy data, real data sets, generate data, other data download |
| Computing with scikit-learn using the calculated sklearn |
Computing strategy for large data sets, computing performance, parallel computing, resource management, and configuration |
二, fire
Corresponding to the previous contents, the contents in the implementation of the methods given in the sklearn.
| Module |
Features |
| sklearn.base module: Base classes and utility functions
sklearn.calibration module: Probability Calibration(标准、标定)
sklearn.cluster: Clustering
sklearn.cluster.bicluster: Biclustering
sklearn.compose: Composite Estimators
sklearn.covariance: Covariance Estimators（协方差）
sklearn.cross_decomposition: Cross decomposition（交叉分解）
sklearn.datasets: Datasets
sklearn.decomposition: Matrix Decomposition
sklearn.discriminant_analysis: Discriminant Analysis（判别分析）
sklearn.dummy: Dummy estimators
sklearn.ensemble: Ensemble Methods
sklearn.exceptions module(exceptions模块): Exceptions and warnings
sklearn.experimental: Experimental
sklearn.feature_extraction: Feature Extraction
sklearn.feature_selection: Feature Selection
sklearn.gaussian_process: Gaussian Processes
sklearn.isotonic: Isotonic regression
sklearn.impute: Impute
sklearn.kernel_approximation Kernel Approximation
sklearn.kernel_ridge Kernel Ridge Regression
sklearn.linear_model: Generalized Linear Models?
sklearn.manifold: Manifold Learning
sklearn.metrics: Metrics
sklearn.mixture: Gaussian Mixture Models
sklearn.model_selection: Model Selection
sklearn.multiclass: Multiclass and multilabel classification
sklearn.multioutput: Multioutput regression and classification
sklearn.naive_bayes: Naive Bayes
sklearn.neighbors: Nearest Neighbors
sklearn.neural_network: Neural network models
sklearn.pipeline: Pipeline
sklearn.inspection: inspection
sklearn.preprocessing: Preprocessing and Normalization
sklearn.random_projection: Random projection?
sklearn.random_projection: Random projection?
sklearn.svm: Support Vector Machines?
sklearn.tree: Decision Trees?
sklearn.utils: Utilities（实用程序） |
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