How deeply does Effective Data Analysis cover scikit-learn?

Demonstrates model fitting and evaluation with linear regression, but stops short of comprehensive explanatory summaries for individual predictors.

Scikit-learn

As explained in Effective Data Analysis

scikit-learn is a Python library providing algorithms and utilities for data preparation, predictive modeling, and machine learning.

Scikit-learn is a Python library for data preparation, predictive modeling, and machine learning. It is especially useful when the goal is accurate prediction rather than detailed explanation of individual predictors. section 9.3.2

A typical predictive workflow uses scikit-learn to:

Split data into training and testing sets, fit the model only on training data, and predict outcomes for unseen test data. section 9.3.2
Compare training and test performance using metrics such as R-squared and root mean squared error (RMSE). Similar results suggest better generalization. section 9.3.2
Choose from a broad collection of algorithms and evaluation metrics, while recognizing that statsmodels provides more comprehensive predictor summaries for explanatory modeling. section 9.3.2

How Effective Data Analysis covers it

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIScikit-learn is an open-source Python library providing classical machine learning algorithms, preprocessing utilities, model evaluation tools, and workflow components.
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Outlier Detection in Pythonscikit-learn is an open-source Python machine-learning library providing estimators, preprocessing utilities, model-selection tools, and related functionality.
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Grokking Machine LearningAn open-source Python library providing classical machine-learning algorithms and utilities.
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Grokking Statisticsscikit-learn is a Python library providing machine-learning algorithms and utilities for data analysis and modeling.

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Related concepts

train_test_splitused by · train_test_split is a scikit-learn function that randomly partitions aligned arrays or datasets into training and testing subsets.
Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
machine learningimplements · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
random forestused by · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
decision treeuses · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.