[Machine Learning] Python Scikit-Learn Library | 파이썬 사이킷런 라이브러리

Python Scikit-Learn Library

파이썬 사이킷런 라이브러리

- Python 언어로 제공되는 오픈소스 머신러닝 라이브러리이다.



* Scikit-learn Official Documentation (URL)

scikit-learn: machine learning in Python — scikit-learn 1.0.1 documentation

 

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Installation (설치) (URL)

# Linux - pip
pip3 install -U scikit-learn

python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
python3 -m pip freeze  # to see all packages installed in the active virtualenv
python3 -c "import sklearn; sklearn.show_versions()"

# Linux - conda
conda create -n sklearn-env -c conda-forge scikit-learn
conda activate sklearn-env

conda list scikit-learn  # to see which scikit-learn version is installed
conda list  # to see all packages installed in the active conda environment
python -c "import sklearn; sklearn.show_versions()"

# Windows - pip
pip install -U scikit-learn

python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
python -m pip freeze  # to see all packages installed in the active virtualenv
python -c "import sklearn; sklearn.show_versions()"

# Windows - conda
conda create -n sklearn-env -c conda-forge scikit-learn
conda activate sklearn-env

conda list scikit-learn  # to see which scikit-learn version is installed
conda list  # to see all packages installed in the active conda environment
python -c "import sklearn; sklearn.show_versions()"

 

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Scikit-learn Classes (Scikit-learn 클래스)

 

Sciki-learn Class	Description
sklearn.base	- Base classes and utility functions
sklearn.calibration	- Probability Calibration
sklearn.cluster	- Clustering
sklearn.compose	- Composite Estimators
sklearn.covariance	- Covariance Estimators
sklearn.cross_decomposition	- Cross decomposition
sklearn.datasets	- Datasets
sklearn.decomposition	- Matrix Decomposition
sklearn.discriminant_analysis	- Discriminant Analysis
sklearn.dummy	- Dummy estimators
sklearn.ensemble	- Ensemble Methods
sklearn.exceptions	- Exceptions and warnings
sklearn.experimental	- Experimental
sklearn.feature_extraction	- Feature Extraction
sklearn.feature_selection	- Feature Selection
sklearn.gaussian_process	- Gaussian Processes
sklearn.impute	- Impute
sklearn.inspection	- Inspection
sklearn.isotonic	- Isotonic regression
sklearn.kernel_approximation	- Kernel Approximation
sklearn.kernel_ridge	- Kernel Ridge Regression
sklearn.linear_model	- Linear Models
sklearn.manifold	- Manifold Learning
sklearn.metrics	- Metrics
sklearn.mixture	- Gaussian Mixture Models
sklearn.model_selection	- Model Selection
sklearn.multiclass	- Multiclass 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.preprocessing	- Preprocessing and Normalization
sklearn.random_projection	- Random projection
sklearn.semi_supervised	- Semi-Supervised Learning
sklearn.svm	- Support Vector Machines
sklearn.tree	- Decision Trees
sklearn.utils	- Utilities Recently deprecated

 

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Classification (분류)

- Identifying which category an object belongs to.
- Spam detection, image recognition, etc
- SVM, Nearest Neighbors, Random Forest, etc

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Regression (회귀)

- Predicting a continuous-valued-attribute associated with an object.
- Drug response, Stock prices predicion, etc
- SVR, Nearest Neighbors, Random Forest, etc

 

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Clustering (클러스터링, 군집화)

- Automatic grouping of similar objects into sets.
- Customer Segmentation, Grouping Experiment Outcomes, etc
- K-Means, Spectral Clustering, Mean-Shift, etc

 

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Dimensionality Reduction (차원 축소)

- Reducing the number of random variables to consider.
- Visualization, Increased Efficiency, etc
- PCA, Feature Selection, Non-Negative Matrix Factorization, etc

 

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Model Selection (모델 선택)

- Comparing, validating and choosing parameters and models.
- Improved accuracy via parameter tuning, etc
- Grid Search, Cross Validation, Metrics, etc

 

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Preprocessing (전처리)

- Feature extraction and normalization.
- Transforming input data such as text for use with machine learning algorithms, etc
- Preprocessing, Feature Extraction, etc

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Reference: scikit-learn.org (URL)