https://github.com/rvigneshwaran/sklearn-implementations

This repository contains the implementation of various algorithms in Machine learning using sklearn library
https://github.com/rvigneshwaran/sklearn-implementations

datascience datascience-machinelearning machine-learning-algorithms machine-translation sklearn sklearn-library

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This repository contains the implementation of various algorithms in Machine learning using sklearn library

• Host: GitHub
• URL: https://github.com/rvigneshwaran/sklearn-implementations
• Owner: rvigneshwaran
• Created: 2023-12-02T15:12:14.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-14T17:00:14.000Z (about 2 years ago)
• Last Synced: 2025-01-18T02:40:48.502Z (about 1 year ago)
• Topics: datascience, datascience-machinelearning, machine-learning-algorithms, machine-translation, sklearn, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 843 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# scikit-learn

scikit-learn is a Python library for machine learning and data analysis. It provides a wide range of algorithms, tools, and utilities to support various tasks in machine learning.

## Table of Contents

1. [Introduction](#introduction)

2. [Installation](#installation)

3. [Basic Concepts](#basic-concepts)

   - Preprocessing

   - Model Selection

   - Linear Models

   - Clustering

   - Decomposition

4. [Intermediate Concepts](#intermediate-concepts)

   - Support Vector Machines (SVM)

   - Decision Trees

   - Ensemble Methods

   - Naive Bayes

   - Semi-Supervised Learning

   - Feature Selection

5. [Advanced Concepts](#advanced-concepts)

   - Neural Networks

   - Nearest Neighbors

   - Gaussian Processes

   - Calibration

   - Text Feature Extraction

   - Imbalanced Data

   - Outlier Detection

   - Multiclass and Multilabel Learning

   - Neural Network Architectures

   - Manifold Learning

6. [Usage](#usage)

7. [Contributing](#contributing)

8. [Documentation](#documentation)

9. [License](#license)

## Introduction

scikit-learn is a powerful and easy-to-use library for machine learning in Python. It is built on top of NumPy, SciPy, and matplotlib, making it an essential tool for data scientists, machine learning practitioners, and researchers. The library offers a vast collection of algorithms and utilities for tasks such as classification, regression, clustering, dimensionality reduction, and more.

## Installation

To install scikit-learn, you can use pip:

```python

pip install scikit-learn

```

For more detailed installation instructions, including setting up virtual environments, refer to the [official installation guide](https://scikit-learn.org/stable/install.html).

## Basic Concepts

### Preprocessing

scikit-learn provides various preprocessing techniques to prepare your data for machine learning. Some commonly used preprocessing techniques include:

- StandardScaler: Standardizes features by removing the mean and scaling to unit variance.

- MinMaxScaler: Scales features to a given range, usually [0, 1].

- OneHotEncoder: Encodes categorical features as one-hot vectors.

- LabelEncoder: Encodes target labels with values between 0 and n_classes-1.

- SimpleImputer: Imputes missing values using mean, median, or most frequent strategy.

- PolynomialFeatures: Generates polynomial features.

### Model Selection

scikit-learn provides tools for model selection, evaluation, and hyperparameter tuning. Some important model selection concepts are:

- train_test_split: Splits data into training and testing sets.

- cross_val_score: Performs cross-validation for model evaluation.

- KFold: Splits data into k folds for cross-validation.

- GridSearchCV: Performs an exhaustive search over a specified parameter grid.

- RandomizedSearchCV: Performs random search over a specified parameter grid.

### Linear Models

scikit-learn supports linear models for both regression and classification tasks. Some common linear models are:

- LinearRegression: Fits a linear model to the training data for regression tasks.

- LogisticRegression: Fits a logistic regression model for binary classification.

### Clustering

scikit-learn offers various clustering algorithms to group data into clusters. Some clustering algorithms include:

- KMeans: Clusters data points into k clusters based on similarity.

- DBSCAN: Density-based spatial clustering of applications with noise.

- AgglomerativeClustering: Hierarchical clustering using a bottom-up approach.

### Decomposition

scikit-learn provides techniques for data decomposition. Two common methods are:

- PCA (Principal Component Analysis): Reduces the dimensionality of data while preserving variance.

- NMF (Non-Negative Matrix Factorization): Decomposes data into non-negative basis vectors.

## Intermediate Concepts

### Support Vector Machines (SVM)

scikit-learn supports Support Vector Machines for classification and regression tasks. Some SVM-based models are:

- SVC (Support Vector Classification): Implements C-Support Vector Classification.

- SVR (Support Vector Regression): Implements epsilon-Support Vector Regression.

- NuSVC (Nu-Support Vector Classification): Similar to SVC but uses a parameter nu for support vector selection.

- NuSVR (Nu-Support Vector Regression): Similar to SVR but uses a parameter nu for support vector selection.

### Decision Trees

scikit-learn provides decision tree-based models for classification and regression tasks. Some decision tree concepts are:

- DecisionTreeClassifier: Fits a decision tree for classification tasks.

- DecisionTreeRegressor: Fits a decision tree for regression tasks.

- ExtraTreesClassifier: Implements an ensemble of randomized decision trees for classification.

- ExtraTreesRegressor: Implements an ensemble of randomized decision trees for regression.

### Ensemble Methods

scikit-learn offers ensemble methods for improving model performance. Some ensemble methods include:

- RandomForestClassifier: Ensemble of decision trees for classification tasks.

- RandomForestRegressor: Ensemble of decision trees for regression tasks.

- GradientBoostingClassifier: Implements gradient boosting for classification.

- GradientBoostingRegressor: Implements gradient boosting for regression.

- AdaBoostClassifier: Implements adaptive boosting for classification.

- AdaBoostRegressor: Implements adaptive boosting for regression.

- VotingClassifier: Combines multiple classifiers for ensemble classification.

- VotingRegressor: Combines multiple regressors for ensemble regression.

- BaggingClassifier: Implements bagging for classification.

- BaggingRegressor: Implements bagging for regression.

- HistGradientBoostingClassifier: Fast and efficient gradient boosting for classification.

- HistGradientBoostingRegressor: Fast and efficient gradient boosting for regression.

### Naive Bayes

scikit-learn supports Naive Bayes classifiers for classification tasks. Some Naive Bayes classifiers are:

- GaussianNB: Implements Gaussian Naive Bayes.

- ComplementNB: Implements Complement Naive Bayes.

- MultinomialNB: Implements Multinomial Naive Bayes.

### Semi-Supervised Learning

scikit-learn offers semi-supervised learning algorithms for using unlabeled data along with labeled data. Some semi-supervised learning algorithms include:

- LabelPropagation: Implements label propagation based on graph theory.

- LabelSpreading: Implements label spreading based on graph theory.

- Self-training: Performs self-training for semi-supervised learning.

- SelfTrainingClassifier: A class that allows user-defined classifier for self-training.

### Feature Selection

scikit-learn provides various techniques for feature selection to improve model performance. Some feature selection techniques include:

- SelectKBest: Selects the top k features based on univariate statistical tests.

- SelectPercentile: Selects the top features based on a specified percentile of univariate statistical tests.

- RFE (Recursive Feature Elimination): Recursively eliminates least important features based on the model performance.

## Advanced Concepts

### Neural Networks

scikit-learn supports Multi-layer Perceptron (MLP) models for classification and regression tasks. Some concepts related to neural networks are:

- MLPClassifier: Multi-layer Perceptron for classification tasks.

- MLPRegressor: Multi-layer Perceptron for regression tasks.

- BernoulliRBM: Restricted Boltzmann Machine for classification.

### Nearest Neighbors

scikit-learn provides nearest neighbors algorithms for classification and regression tasks. Some nearest neighbors algorithms include:

- KNeighborsClassifier: k-Nearest Neighbors classifier for classification.

- KNeighborsRegressor: k-Nearest Neighbors regressor for regression.

- RadiusNeighborsClassifier: Radius-based nearest neighbors classifier.

- RadiusNeighborsRegressor: Radius-based nearest neighbors regressor.

- NearestCentroid: Nearest Centroid classifier.

### Gaussian Processes

scikit-learn offers Gaussian Processes for classification and regression tasks. Some Gaussian Process models include:

- GaussianProcessClassifier: Gaussian Process for classification tasks.

- GaussianProcessRegressor: Gaussian Process for regression tasks with L-BFGS-B solver.

### Calibration

scikit-learn provides calibration methods to calibrate model probabilities. One calibration method is:

- CalibratedClassifierCV: Calibrates probabilities of a classifier using cross-validation.

### Text Feature Extraction

scikit-learn supports various techniques for text feature extraction. Some text feature extraction techniques are:

- CountVectorizer: Converts text into a matrix of token counts.

- TfidfVectorizer: Converts text into a matrix of TF-IDF features.

### Imbalanced Data

scikit-learn offers techniques to handle imbalanced datasets. Some imbalanced data techniques include:

- SMOTE: Synthetic Minority Over-sampling Technique for oversampling the minority class.

- ADASYN: Adaptive Synthetic Sampling for oversampling the minority class.

### Outlier Detection

scikit-learn provides algorithms for outlier detection. Some outlier detection algorithms include:

- IsolationForest: Detects outliers using Isolation Forest algorithm.

- LocalOutlierFactor: Detects outliers using Local Outlier Factor.

### Multiclass and Multilabel Learning

scikit-learn supports algorithms for multiclass and multilabel classification. Some concepts for multiclass and multilabel learning include:

- OneVsOneClassifier: One-vs-one multiclass strategy.

- OneVsRestClassifier: One-vs-rest multiclass strategy.

- OutputCodeClassifier: Output code multiclass strategy.

### Neural Network Architectures

scikit-learn supports various neural network architectures. Some neural network architectures include:

- MLPClassifier with adaptive learning rate (MLPClassifier with 'adam' solver).

- MLPClassifier with Stochastic Gradient Descent (MLPClassifier with 'sgd' solver).

- MLPRegressor with adaptive learning rate (MLPRegressor with 'adam' solver).

- MLPRegressor with Stochastic Gradient Descent (MLPRegressor with 'sgd' solver).

### Manifold Learning

scikit-learn provides manifold learning techniques for dimensionality reduction. Some manifold learning techniques are:

- LocallyLinearEmbedding (LLE)

- Isomap

## Usage

For detailed usage and examples, please refer to the [official documentation](https://scikit-learn.org/stable/).

## Contributing

If you want to contribute to scikit-learn, please check the [contributing guidelines](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.rst).

## Documentation

For more information and detailed documentation, visit the [scikit-learn website](https://scikit-learn.org/stable/).

## License

scikit-learn is distributed under the 3-clause BSD license. See the [LICENSE](https://github.com/scikit-learn/scikit-learn/blob/main/COPYING) file for more details.