Getting Started with Scikit-Learn
Chapter 1: Introduction to Scikit-learn and Setup
What is Scikit-learn?
Installation and Environment Setup
Overview of the Scikit-learn API
Data Representation in Scikit-learn
Loading Example Datasets
Hands-on Practical: Setup Verification
Quiz for Chapter 1
Chapter 2: Supervised Learning: Regression
Introduction to Regression Problems
Linear Regression Fundamentals
Implementing Linear Regression with Scikit-learn
Interpreting Model Coefficients
Regression Evaluation Metrics
Calculating Metrics in Scikit-learn
Hands-on Practical: Building a Regression Model
Quiz for Chapter 2
Chapter 3: Supervised Learning: Classification
Introduction to Classification Problems
Logistic Regression for Classification
K-Nearest Neighbors (KNN) Algorithm
Implementing KNN with Scikit-learn
Support Vector Machines (SVM) Basics
Implementing SVM with Scikit-learn
Classification Evaluation Metrics
Calculating Metrics in Scikit-learn
Hands-on Practical: Building Classification Models
Quiz for Chapter 3
Chapter 4: Data Preprocessing and Feature Engineering
The Importance of Data Preprocessing
Feature Scaling Techniques
Applying Scalers in Scikit-learn
Encoding Categorical Features
Applying Encoders in Scikit-learn
Handling Missing Values
Using Imputers in Scikit-learn
Hands-on Practical: Preprocessing Data
Quiz for Chapter 4
Chapter 5: Model Selection and Evaluation
The Problem of Overfitting and Underfitting
Splitting Data: Training and Testing Sets
Introduction to Cross-Validation
Implementing K-Fold Cross-Validation
Stratified K-Fold for Classification
Grid Search for Hyperparameter Tuning
Hands-on Practical: Model Evaluation and Selection
Quiz for Chapter 5
Chapter 6: Building Pipelines
Motivation for Using Pipelines
Creating a Simple Pipeline
Accessing Pipeline Steps
Using Pipelines with Cross-Validation
Grid Search with Pipelines
Constructing Complex Pipelines with ColumnTransformer
Hands-on Practical: Pipeline Construction and Tuning
Quiz for Chapter 6
Grid Search with Pipelines
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- Tuning the hyperparameters of an estimator, Scikit-learn Developers, 2023 - Details the integration of
GridSearchCVwithPipelineobjects, including thestep_name__parameter_namesyntax and recommended practices. - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - Offers practical examples and explanations of using Scikit-learn pipelines and grid search for hyperparameter adjustment.
- Scikit-learn: Machine Learning in Python, Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournaupeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay, 2011 Journal of Machine Learning Research, Vol. 12 (Journal of Machine Learning Research) DOI: 10.5555/1953048.2078195 - Introduces the Scikit-learn library, whose modular architecture supports the creation and optimization of pipelines.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) DOI: 10.1007/b94608 - Presents theoretical foundations for cross-validation and hyperparameter selection, essential for understanding the principles behind
GridSearchCVand pipeline validation.
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