scikit-learn
Audited by Runlayer on Feb 21, 2026
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They enable reproducible workflows and seamless integration with cross-validation and hyperparameter tuning.
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Tool: references/preprocessing.md [1/2] Description: # Data Preprocessing and Feature Engineering Reference ## Overview Data preprocessing transforms raw data into a format suitable for machine learning models.
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Description: Use | |---------|-----------|-------------| | Known K, spherical | K-Means | Fast, simple | | Unknown K, arbitrary shapes | DBSCAN | Noise/outliers present | | Hierarchical structure | Agglomerative | Need dendrogram | | Soft clustering | Gaussian Mixture | Probability estimates | ### Dimensionality Reduction | Problem | Algorithm | When to Use | |---------|-----------|-------------| | Linear reduction | PCA | Variance explanation | | Visualization | t-SNE | 2D/3D plots | | Non-nega
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Tool: references/supervised_learning.md [2/2] Description: = MultinomialNB(alpha=1.0) # alpha is smoothing parameter model_multinomial.fit(X_train, y_train) ``` ## Neural Networks **MLPClassifier / MLPRegressor** - Multi-layer perceptron (feedforward neural network) - Key parameters: - `hidden_layer_sizes`: Tuple of hidden layer sizes, e.g., (100, 50) - `activation`: 'relu', 'tanh', 'logistic' - `solver`: 'adam', 'sgd', 'lbfgs' - `alpha`: L2 regularization parameter - `learning_rate`: 'constant'
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Tool: references/unsupervised_learning.md [1/2]
Tool: references/unsupervised_learning.md [2/2]
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Tool: scripts/classification_pipeline.py Description: """ Complete classification pipeline example with preprocessing, model training, hyperparameter tuning, and evaluation.
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Tool: scripts/clustering_analysis.py Description: """ Clustering analysis example with multiple algorithms, evaluation, and visualization.
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