In-depth Scikit-learn: Master Python's most powerful machine learning library (nanny-level tutorial)

Basic usage

1. Installation and import: Get startedScikit-learnPreviously, the installation library could be usedpipTo install:

2. pip install scikit-learn
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3. Then, import Scikit-learn by:

   import sklearn
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The data in Scikit-learn is usuallyTwo-dimensional array(or matrix) form is represented by a feature matrix. Usually usedXRepresents the feature matrix,yRepresents the target array (ifSupervised learningTask)

Data representation: The data in Scikit-learn is usually represented in the form of a two-dimensional array (or matrix) and is called a feature matrix. Usually usedXRepresents the feature matrix,yRepresents the target array (if there is a supervised learning task)

X = [[feature1, feature2, ...],



     [feature1, feature2, ...],



     ...]



 



y = [target1, target2, ...]

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Model establishment and training: Using Scikit-learn to build a model usually follows the following steps:

• Select the appropriate model class (such as linear regression, decision tree, support vector machine, etc.) and import it
• Fit the model with data, that is, "train" the model with data to generate a learning model

from sklearn.model_selection import train_test_split




from  import SVC



 



# Divide the training set and the test set



X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)



 



# Create a Support Vector Machine Classifier and fit the data



model = SVC(kernel='linear')



(X_train, y_train)

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 Forecasting and evaluation: Use a trained model to predict and evaluate itperformance

y_pred = (X_test)



 




from  import accuracy_score



accuracy = accuracy_score(y_test, y_pred)

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Things to note

1. Data preprocessing: In applicationMachine LearningBefore the algorithm, data preprocessing is usually required, such as missing value processing, feature scaling, encoding categorical variables, etc. Scikit-learn provides a wealth of tools and pipeline capabilities to simplify these tasks

2. Hyperparameter tuning: Each machine learning model has some regulating parameters (hyperparameters), which are not learned by the model itself, but are manually set by the user. Correct selection and tuning of hyperparameters is critical to model performance, Scikit-learn provides a variety of parameter tuning tools and techniques such as cross-validation and grid search to help optimize models

3. Model evaluation: When selecting a model, it is necessary to consider not only its performance on the training set, but also its generalization ability in the test set or cross-validation. Scikit-learn provides a variety of evaluation metrics and techniques (such as cross-validation) to help evaluate model performance

4. Extended features and integration: Scikit-learn not only supports standard supervised and unsupervised learning algorithms, but also provides advanced features such as feature selection, dimensionality reduction, manifold learning, pipeline and model persistence. In addition, it can be used with other Python data science libraries (e.g.NumPy, Pandas and Matplotlib) are well integrated to make it a complete solution for data science tasks

Some interesting development backgrounds and facts

• The power of open source communities: Scikit-learn is an open source project composed of volunteer contributors. It was originally launched in 2007 by David Cournapeau and has received positive contributions from many data scientists and developers over the next few years. This open source community collaborative spirit has allowed Scikit-learn to grow rapidly and become one of the most popular machine learning libraries in the Python ecosystem.

• Important tools for education and research: Scikit-learn is not just a tool for practical industrial applications, it also plays an important role in education and academic research. Many universities and research institutions use Scikit-learn to teach the basics of machine learning, as well as conduct academic research. Its concise API design and extensive documentation enable students and researchers to get started and apply quickly

• Community-driven development: The development of Scikit-learn is not only driven by the core development team, but also benefits from feedback and contributions from developers and user communities around the world. This open and transparent approach to development allows Scikit-learn to quickly adapt to the development of new technologies and algorithms, maintaining its leading position in machine learning

• Integration with other tools: Scikit-learn not only works with other tools in the Python ecosystem (such as NumPy,PandasHighly integrated with Matplotlib, and also supports big data frameworks such as ApacheSpark)andDeep Learning FrameworkIntegrations such as TensorFlow and PyTorch. This flexibility makes Scikit-learn a machine learning tool that is widely used in different environments and needs