Scikit-learn
scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Scikit-learn is an open-source Python library providing classical machine learning algorithms, preprocessing utilities, model evaluation tools, and workflow components.
- How it is used
- Supports repeatable tabular-data workflows that preprocess, train, cross-validate, and refit machine-learning models.
- How deep it goes
- Explains the common estimator API and workflow, while deferring lengthy mathematics and model-specific theory.
- Examples
- Demonstrates synthetic binary classification with StandardScaler and LogisticRegression, plus Airbnb listing-value classification with gradient boosting.
- What you can do after
- Apply it via scikit-learn to assemble pipelines, evaluate models with cross-validation, and refit suitable tabular-data models.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains scikit-learn →
Effective Data Analysis
scikit-learn is a Python library providing algorithms and utilities for data preparation, predictive modeling, and machine learning.
- How it is used
- Supports predictive workflows that prepare data, fit algorithms, generate forecasts, and evaluate performance on unseen observations.
- How deep it goes
- Demonstrates model fitting and evaluation with linear regression, but stops short of comprehensive explanatory summaries for individual predictors.
- Examples
- Uses rat sightings and weather predictors to demonstrate linear regression, held-out predictions, R-squared, and RMSE.
- What you can do after
- Apply it via Python to split datasets, train predictive models, generate test predictions, and compare regression metrics.
Outlier Detection in Python
scikit-learn is an open-source Python machine-learning library providing estimators, preprocessing utilities, model-selection tools, and related functionality.
- How it is used
- Supports tabular outlier-detection projects with estimators, data preparation, evaluation, and standardized prediction outputs.
- How deep it goes
- Explains four detectors, their API outputs, preprocessing requirements, and comparative use, but does not detail their internal algorithms.
- Examples
- Uses Isolation Forest, Local Outlier Factor, one-class Support Vector Machine, Elliptic Envelope, make_classification(), and make_blobs().
- What you can do after
- Apply it via scikit-learn to prepare tabular data, run multiple outlier detectors, and inspect scores and inlier or outlier labels.
Read how Outlier Detection in Python explains scikit-learn →
Grokking Machine Learning
An open-source Python library providing classical machine-learning algorithms and utilities.
- How it is used
- Supports practical workflows for training, testing, evaluating, and tuning classical machine-learning models.
- How deep it goes
- Demonstrates model fitting, graphical boundaries, accuracy, and selected hyperparameters without detailing library internals.
- Examples
- Uses a two-feature triangle-and-square dataset, a linear SVM dataset, and Pandas DataFrames containing numerical features and labels.
- What you can do after
- Apply it via Python to fit classifiers, inspect boundaries, adjust hyperparameters, and evaluate model performance.
Grokking Statistics
scikit-learn is a Python library providing machine-learning algorithms and utilities for data analysis and modeling.
- How it is used
- Fits logistic regression models for binary classification and probability prediction in Python.
- How deep it goes
- Explains fitting, input shaping, penalties, and parameter access, but defers the mathematics of maximum likelihood estimation.
- Examples
- Uses toddler meltdown data, hours without a nap, NumPy arrays, fitted coefficients, a plotted curve, and a 4.78-hour prediction.
- What you can do after
- Apply logistic regression via scikit-learn, inspect fitted parameters, plot the model, and generate outcome probabilities.
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 with readable, indentation-based 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.
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