# apachecn/sklearn-doc-zh

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## Links

- GitHub: https://github.com/apachecn/sklearn-doc-zh
- Homepage: http://sklearn.apachecn.org
- awesome-repositories: https://awesome-repositories.com/repository/apachecn-sklearn-doc-zh.md

## Topics

`documentation` `machine-learning` `python` `scikit-learn`

## Description

This project provides a translated version of the scikit-learn machine learning library guides and API references for Chinese speakers. It serves as a localized knowledge base and technical reference for implementing predictive data analysis and statistical modeling using a Python-based toolkit.

The resource covers the implementation of supervised learning, including classification and regression tasks, and unsupervised learning workflows for pattern discovery and anomaly detection. It also provides guidance on data science education, specifically focusing on the use of scikit-learn for machine learning.

The documentation includes detailed instructions on data preprocessing, dimensionality reduction, and feature selection. It further details model evaluation and tuning through performance metrics, hyperparameter optimization, and generalization validation, as well as the use of prediction pipelines and natural language processing utilities.

## Tags

### Mobile Development

- [Framework Documentation Translations](https://awesome-repositories.com/f/mobile-development/android-ecosystem/android-development/chinese-learning-resources/machine-learning-theory-translations/framework-documentation-translations.md) — Provides a comprehensive Chinese translation of the scikit-learn machine learning library guides and API references.

### Artificial Intelligence & ML

- [Decision Trees](https://awesome-repositories.com/f/artificial-intelligence-ml/decision-trees.md) — Provides comprehensive guides on creating tree-based models for both classification and regression tasks. ([source](https://sklearn.apachecn.org/master/1))
- [Ensemble Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/ensemble-learning.md) — Details the use of bagging, boosting, and random forests to combine multiple estimators for improved accuracy. ([source](https://sklearn.apachecn.org/examples/))
- [Clustering Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/k-means-clustering/clustering-algorithms.md) — Documents the grouping of unlabeled data using algorithms like K-Means, DBSCAN, and biclustering. ([source](https://sklearn.apachecn.org/examples))
- [Linear Regression Models](https://awesome-repositories.com/f/artificial-intelligence-ml/linear-regression-models.md) — Describes fitting data using generalized linear models, including regularization techniques like Lasso and Ridge. ([source](https://sklearn.apachecn.org/master/1))
- [Machine Learning Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-classification.md) — Documents the implementation of supervised learning models to assign predefined labels to data points. ([source](https://sklearn.apachecn.org/examples))
- [Model Evaluation and Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-and-tuning.md) — Provides detailed instructions on measuring model performance and optimizing hyperparameters for better prediction accuracy.
- [Numerical Regressions](https://awesome-repositories.com/f/artificial-intelligence-ml/numerical-regressions.md) — Documents the process of predicting continuous numerical values using decision trees, ridge regression, and Gaussian processes. ([source](https://sklearn.apachecn.org/examples/))
- [Scikit-Learn Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/scikit-learn-implementations.md) — Provides implementation guides for classical supervised learning algorithms like classification and regression.
- [Localized Documentation](https://awesome-repositories.com/f/artificial-intelligence-ml/scikit-learn-implementations/localized-documentation.md) — Provides a translated version of the scikit-learn machine learning library guides and API references for Chinese speakers.
- [Support Vector Machines](https://awesome-repositories.com/f/artificial-intelligence-ml/support-vector-machines.md) — Details the construction of boundaries that maximize the margin between classes for classification and novelty detection. ([source](https://sklearn.apachecn.org/master/1))
- [Unsupervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/unsupervised-learning.md) — Guides users through unsupervised learning workflows for pattern discovery, clustering, and anomaly detection.
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction.md) — Provides technical references for simplifying complex datasets by projecting them into lower dimensions. ([source](https://sklearn.apachecn.org/examples))
- [Feature Selection Methods](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-selection-methods.md) — Offers guidance on identifying the most relevant variables in a dataset using various selection methods. ([source](https://sklearn.apachecn.org/examples))
- [Gaussian Processes](https://awesome-repositories.com/f/artificial-intelligence-ml/gaussian-processes.md) — Documents probabilistic models used for regression and uncertainty quantification via Gaussian processes. ([source](https://sklearn.apachecn.org/master/1))
- [K-Nearest Neighbor Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/k-nearest-neighbor-classifiers.md) — Details the implementation of proximity-based prediction for assigning labels or values in the feature space. ([source](https://sklearn.apachecn.org/master/1))
- [Regression Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-classification/neural-network-classification/regression-neural-networks.md) — Provides documentation for implementing multi-layer perceptrons for both classification and numerical regression tasks. ([source](https://sklearn.apachecn.org/master/1))
- [Hyperparameter Search Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/model-fine-tuning-resources/hyperparameter-tuning/hyperparameter-search-strategies.md) — Details algorithmic strategies like grid search and random search for finding optimal model configurations. ([source](https://sklearn.apachecn.org/examples))
- [Validation Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/validation-evaluators.md) — Explains processes for estimating out-of-sample performance using cross-validation and training-test split strategies. ([source](https://sklearn.apachecn.org/examples))
- [Naive Bayes Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/naive-bayes-classifiers.md) — Covers probabilistic classification implementation based on Bayes theorem and feature independence. ([source](https://sklearn.apachecn.org/master/1))
- [Performance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/performance-metrics.md) — Provides detailed documentation on calculating and visualizing statistical performance indicators like ROC curves and precision-recall metrics. ([source](https://sklearn.apachecn.org/examples))
- [Prediction Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-pipelines.md) — Explains how to chain preprocessing steps and estimators into a unified workflow for streamlined data transformation. ([source](https://sklearn.apachecn.org/examples))
- [Stochastic Gradient Descent](https://awesome-repositories.com/f/artificial-intelligence-ml/stochastic-gradient-descent.md) — Explains iterative optimization using stochastic gradient descent to train linear models on large-scale datasets. ([source](https://sklearn.apachecn.org/master/1))
- [Linear Discriminant Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-classification/dimensionality-reduction/linear-discriminant-analysis.md) — Explains the use of linear and quadratic discriminant analysis for class separation and dimensionality reduction. ([source](https://sklearn.apachecn.org/master/1))
- [Text Feature Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/text-feature-extraction.md) — Offers instructions on transforming unstructured text into numerical features using techniques like hashing and sparse matrices. ([source](https://sklearn.apachecn.org/examples))

### Data & Databases

- [Anomaly Detection Algorithms](https://awesome-repositories.com/f/data-databases/anomaly-detection-algorithms.md) — Provides instructions on identifying unusual data points using algorithms such as isolation forests and local outlier factors. ([source](https://sklearn.apachecn.org/examples))
- [Data Preprocessing Pipelines](https://awesome-repositories.com/f/data-databases/data-preprocessing-pipelines.md) — Describes how to chain scaling and imputation steps into a unified pipeline for model ingestion.
- [Missing Value Imputation](https://awesome-repositories.com/f/data-databases/missing-value-imputation.md) — Explains techniques for filling missing data gaps using iterative estimators to maintain dataset integrity. ([source](https://sklearn.apachecn.org/examples))

### Education & Learning Resources

- [Framework Implementation Guides](https://awesome-repositories.com/f/education-learning-resources/framework-implementation-guides.md) — Offers detailed instructions on using a Python-based toolkit for predictive data analysis and statistical modeling.
- [Technical Library Documentation](https://awesome-repositories.com/f/education-learning-resources/technical-library-documentation.md) — Provides detailed technical references and tutorials for implementing machine learning algorithms via a standard library.
- [Data Science Resources](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/data-science-resources.md) — Serves as a localized knowledge base for learning essential data science and analytical modeling techniques.

### Part of an Awesome List

- [Data Preprocessing for Modeling](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning-and-data-science/data-preprocessing-for-modeling.md) — Details methods for preparing raw datasets through feature scaling, discretization, and normal distribution mapping. ([source](https://sklearn.apachecn.org/examples))