Machine Learning Models Testing for Scikit-Learn

Introduction to scikit-learn

Sk-learn or scikit-learn is a widely used Python machine learning library that offers a comprehensive range of tools for various tasks in the data science workflow. It is built on top of popular scientific computing libraries like NumPy, SciPy, and Matplotlib, which makes it highly efficient and easily integrated into existing data science pipelines. Let’s explore the key features and functionalities of sk-learn:

• Dataset loading and preprocessing: This library provides a rich collection of datasets for experimentation, including well-known benchmark datasets, such as Iris, Boston Housing, and MNIST. It simplifies the process of loading and preprocessing data by offering functions for tasks like splitting data into training and testing sets, feature scaling, and handling missing values.

• Feature extraction and selection: This library offers an extensive set of techniques for extracting and selecting features from raw data. These techniques include methods for dimensionality reduction, like principal component analysis (PCA), feature selection algorithms, such as SelectKBest, and tools for feature engineering, such as PolynomialFeatures. ...