{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-analytic-signal-snippets-scikit-learn","slug":"scikit-learn-snippets","name":"Scikit-learn Snippets","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=analytic-signal.snippets-scikit-learn","page_url":"https://unfragile.ai/scikit-learn-snippets","categories":["code-editors"],"tags":["__web_extension","data science","machine learning","python","scikit-learn","snippet"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_0","uri":"capability://code.generation.editing.trigger.based.scikit.learn.code.snippet.insertion","name":"trigger-based scikit-learn code snippet insertion","description":"Provides static code templates for scikit-learn workflows that are inserted into the editor via prefix triggers (e.g., `sk-regress`, `sk-classify`). When a user types a trigger prefix in a Python file, VS Code's IntelliSense system displays matching snippets; selecting one inserts the template at the cursor position with tab-stop placeholders for manual parameter configuration. The extension leverages VS Code's native snippet syntax (TextMate-compatible) to enable rapid navigation through placeholder arguments using the Tab key.","intents":["I want to quickly scaffold a scikit-learn regression model without typing boilerplate imports and fit() calls","I need to insert a cross-validation pipeline template and customize parameters for my dataset","I'm building a classification workflow and want to avoid looking up the exact API syntax for model creation"],"best_for":["Python developers new to scikit-learn who want to reduce API lookup time","Data scientists prototyping ML pipelines in VS Code who value keyboard-driven workflows","Teams standardizing on scikit-learn with consistent code patterns across projects"],"limitations":["Snippets are entirely static — no dynamic parameter inference based on data shape or type","No syntax validation or linting of inserted code; malformed templates are inserted as-is","Requires manual placeholder substitution; no intelligent argument suggestion based on project context","No awareness of existing imports or variable names in the file; may create duplicate imports"],"requires":["Visual Studio Code (version not specified, likely 1.50+)","Python file context (.py or .ipynb Jupyter notebook)","scikit-learn library installed in the Python environment (not enforced by extension)"],"input_types":["text (trigger prefix typed in editor)","user selection from IntelliSense dropdown"],"output_types":["code (Python snippet with tab-stop placeholders)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_1","uri":"capability://code.generation.editing.model.creation.snippet.templates.for.supervised.learning","name":"model-creation snippet templates for supervised learning","description":"Provides pre-written code templates for instantiating and fitting scikit-learn regression and classification models (e.g., LinearRegression, RandomForestClassifier, SVC). Each template includes model initialization with default hyperparameters, data fitting via `.fit(X, y)`, and prediction via `.predict()`. 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Templates are triggered via `sk-cluster`, `sk-embed`, `sk-density`, and `sk-anomaly` prefixes and include model instantiation, fitting, and prediction/transformation steps with customizable parameters.","intents":["I want to apply PCA for dimensionality reduction without looking up the exact sklearn.decomposition import and fit_transform() syntax","I need to cluster data using KMeans and want a template that includes both fitting and label assignment","I'm exploring anomaly detection and want quick templates for Isolation Forest and Local Outlier Factor to compare approaches"],"best_for":["Data scientists exploring unsupervised learning techniques without deep API familiarity","Practitioners building feature engineering pipelines that include dimensionality reduction","Teams standardizing anomaly detection or clustering approaches across projects"],"limitations":["No automatic parameter selection based on data dimensionality or cluster count estimation","Density estimation templates do not include model selection or likelihood evaluation code","Anomaly detection templates do not include threshold tuning or contamination parameter guidance","No visualization templates for cluster visualization or dimensionality reduction results"],"requires":["VS Code with Python extension","scikit-learn library installed","Unlabeled data in variable X (or user must rename placeholder)"],"input_types":["text (trigger `sk-cluster`, `sk-embed`, `sk-density`, or `sk-anomaly`)"],"output_types":["code (Python unsupervised learning code with placeholders for model type and parameters)"],"categories":["code-generation-editing","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_3","uri":"capability://code.generation.editing.data.preprocessing.and.input.handling.snippet.templates","name":"data preprocessing and input handling snippet templates","description":"Provides code templates for common data preprocessing workflows including data loading, feature scaling, encoding categorical variables, handling missing values, and feature engineering. Templates are triggered via `sk-read` (data loading) and `sk-prep` (preprocessing) prefixes and include imports, function calls, and placeholder variables for dataset paths, feature names, and preprocessing parameters. Templates leverage scikit-learn's preprocessing module (StandardScaler, MinMaxScaler, OneHotEncoder, LabelEncoder, SimpleImputer) and pandas integration patterns.","intents":["I want to load a CSV file and apply standard scaling to numeric features without typing the full sklearn.preprocessing import and fit_transform() sequence","I need to encode categorical variables using OneHotEncoder and want a template that handles both fitting and transformation","I'm building a preprocessing pipeline and want quick templates for handling missing values and feature scaling"],"best_for":["Data scientists building preprocessing pipelines in Jupyter notebooks","ML practitioners who want to standardize feature scaling and encoding across projects","Teams enforcing consistent data preparation patterns before model training"],"limitations":["Templates assume CSV input format; no templates for other data sources (SQL, Parquet, APIs)","No automatic detection of numeric vs categorical columns; user must manually select appropriate preprocessor","Missing value handling templates do not include strategy selection guidance (mean, median, forward-fill, etc.)","No templates for feature engineering (polynomial features, interaction terms, domain-specific transformations)"],"requires":["VS Code with Python extension","scikit-learn and pandas libraries installed","Data file accessible at a path the user specifies in the template"],"input_types":["text (trigger `sk-read` or `sk-prep`)","file path (specified in template placeholder)"],"output_types":["code (Python preprocessing code with placeholders for file paths, column names, and parameters)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_4","uri":"capability://code.generation.editing.model.validation.and.cross.validation.snippet.templates","name":"model validation and cross-validation snippet templates","description":"Provides code templates for model evaluation workflows including cross-validation (k-fold, stratified k-fold, time-series split), train/test splitting, metric calculation (accuracy, precision, recall, F1, ROC-AUC, MSE, R²), and hyperparameter tuning (GridSearchCV, RandomizedSearchCV). Templates are triggered via `sk-validation` prefix and include imports, function calls, and tab-stops for customizing fold counts, test set size, scoring metrics, and parameter grids.","intents":["I want to perform 5-fold cross-validation on my model without typing the full sklearn.model_selection.cross_val_score() call","I need to evaluate my classifier using multiple metrics (accuracy, precision, recall, F1) and want a template that computes all of them","I'm tuning hyperparameters and want a GridSearchCV template with placeholders for the parameter grid and scoring metric"],"best_for":["ML practitioners building robust model evaluation pipelines","Data scientists comparing model performance across multiple metrics","Teams standardizing cross-validation and hyperparameter tuning workflows"],"limitations":["Templates do not include automatic metric selection based on problem type (binary vs multiclass vs regression)","No guidance on appropriate fold counts or test set sizes for different dataset sizes","GridSearchCV templates require manual specification of parameter grids; no automated parameter space generation","No templates for advanced validation techniques (nested cross-validation, learning curves, permutation importance)"],"requires":["VS Code with Python extension","scikit-learn library installed","Trained model and test data in memory (or user must modify template to load them)"],"input_types":["text (trigger `sk-validation`)"],"output_types":["code (Python cross-validation and metric calculation code with placeholders)"],"categories":["code-generation-editing","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_5","uri":"capability://code.generation.editing.model.inspection.and.explainability.snippet.templates","name":"model inspection and explainability snippet templates","description":"Provides code templates for model introspection and interpretation including feature importance extraction (for tree-based models), coefficient inspection (for linear models), permutation importance calculation, and model metadata inspection (get_params, get_feature_names_out). Templates are triggered via `sk-inspect` prefix and include imports, function calls, and tab-stops for customizing feature names, importance thresholds, and output formatting.","intents":["I want to extract feature importances from my Random Forest model and visualize them without typing the full .feature_importances_ access pattern","I need to inspect the coefficients of my logistic regression model to understand feature contributions","I'm debugging my model and want to quickly check its hyperparameters using get_params()"],"best_for":["Data scientists building interpretable ML models and needing quick access to feature importance","ML practitioners debugging model behavior by inspecting learned parameters","Teams documenting model decisions and feature contributions for stakeholders"],"limitations":["Templates assume tree-based or linear models; no support for black-box model interpretation (SHAP, LIME)","Feature importance templates do not include visualization code; user must add plotting manually","No templates for partial dependence plots or accumulated local effects (ALE) plots","Permutation importance templates do not include guidance on baseline metric selection or scoring function choice"],"requires":["VS Code with Python extension","scikit-learn library installed","Trained model object in memory"],"input_types":["text (trigger `sk-inspect`)"],"output_types":["code (Python model inspection code with placeholders for feature names and thresholds)"],"categories":["code-generation-editing","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_6","uri":"capability://code.generation.editing.model.serialization.and.persistence.snippet.templates","name":"model serialization and persistence snippet templates","description":"Provides code templates for saving and loading trained scikit-learn models using joblib and pickle, including model export, model loading, and metadata persistence. Templates are triggered via `sk-io` prefix and include imports, function calls, and tab-stops for customizing file paths, compression settings, and variable names. Templates cover both joblib (recommended for scikit-learn) and pickle approaches with guidance on when to use each.","intents":["I want to save my trained model to disk using joblib without looking up the exact joblib.dump() syntax","I need to load a previously saved model and want a template that handles both the model and any preprocessing objects","I'm deploying a model and want to quickly save it with compression to reduce file size"],"best_for":["ML practitioners building production pipelines that require model persistence","Data scientists sharing trained models across notebooks or team members","Teams standardizing model serialization formats and storage locations"],"limitations":["Templates do not include version compatibility checking (scikit-learn version mismatch can break loaded models)","No templates for model registry integration (MLflow, Weights & Biases, Model Hub)","No guidance on serializing preprocessing pipelines (Pipeline objects) alongside models","No templates for cloud storage integration (S3, GCS, Azure Blob Storage)"],"requires":["VS Code with Python extension","scikit-learn and joblib libraries installed","Trained model object in memory (for saving) or file path (for loading)"],"input_types":["text (trigger `sk-io`)"],"output_types":["code (Python model save/load code with placeholders for file paths and compression settings)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_7","uri":"capability://code.generation.editing.hyperparameter.configuration.and.selection.snippet.templates","name":"hyperparameter configuration and selection snippet templates","description":"Provides code templates for defining and exploring hyperparameter spaces, including parameter grid definition for GridSearchCV and RandomizedSearchCV, parameter range specification, and parameter validation. Templates are triggered via `sk-args` prefix and include lists of valid hyperparameter options for common scikit-learn models (e.g., kernel options for SVM, criterion options for decision trees, solver options for logistic regression). Templates serve as reference guides for valid parameter values without requiring API documentation lookup.","intents":["I want to define a parameter grid for GridSearchCV and need a reference for valid hyperparameter values for my model","I'm tuning a Random Forest and want to see the valid options for criterion, max_depth, and n_estimators without consulting the docs","I need to understand what solver options are available for Logistic Regression and want a template showing them"],"best_for":["ML practitioners tuning hyperparameters who want quick reference to valid parameter options","Data scientists learning scikit-learn API and wanting to explore available hyperparameters","Teams standardizing hyperparameter ranges across projects"],"limitations":["Templates provide only valid parameter names and options; no guidance on parameter selection strategy or tuning ranges","No templates for parameter dependencies (e.g., max_depth only relevant if max_features < n_features)","No support for custom parameter spaces or conditional parameters","No integration with hyperparameter optimization libraries (Optuna, Hyperopt, Ray Tune)"],"requires":["VS Code with Python extension","scikit-learn library installed (for reference only; templates are static)"],"input_types":["text (trigger `sk-args`)"],"output_types":["code (Python parameter grid definition with valid option lists)"],"categories":["code-generation-editing","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-analytic-signal-snippets-scikit-learn__cap_8","uri":"capability://code.generation.editing.module.import.and.setup.snippet.templates","name":"module import and setup snippet templates","description":"Provides code templates for importing scikit-learn modules, setting default configurations, and initializing common dependencies (numpy, pandas, matplotlib). Templates are triggered via `sk-setup` prefix and include standard import statements for scikit-learn submodules (linear_model, tree, ensemble, preprocessing, model_selection, metrics), numpy, pandas, and visualization libraries. Templates serve as boilerplate for new Python files or notebooks to avoid repetitive import typing.","intents":["I'm starting a new Jupyter notebook and want to quickly insert all the standard scikit-learn, numpy, and pandas imports","I need to import specific scikit-learn submodules (e.g., sklearn.ensemble, sklearn.metrics) without typing them manually","I want to set up a Python file with standard ML library imports and default configuration (e.g., random seed, matplotlib style)"],"best_for":["Data scientists starting new notebooks or Python files who want to avoid repetitive import typing","Teams standardizing import organization and module structure across projects","ML practitioners who want consistent setup across multiple notebooks"],"limitations":["Templates include only standard imports; no support for optional dependencies (e.g., XGBoost, LightGBM, Optuna)","No automatic detection of required modules based on code context; user must manually select which imports to include","Templates do not include environment variable setup or configuration file loading","No support for conditional imports based on library availability"],"requires":["VS Code with Python extension","scikit-learn, numpy, pandas libraries installed (or user must comment out unused imports)"],"input_types":["text (trigger `sk-setup`)"],"output_types":["code (Python import statements and configuration code)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (version not specified, likely 1.50+)","Python file context (.py or .ipynb Jupyter notebook)","scikit-learn library installed in the Python environment (not enforced by extension)","VS Code with Python extension","scikit-learn library installed","Pre-loaded training data in variables named X and y (or user must rename placeholders)","Unlabeled data in variable X (or user must rename placeholder)","scikit-learn and pandas libraries installed","Data file accessible at a path the user specifies in the template","Trained model and test data in memory (or user must modify template to load them)"],"failure_modes":["Snippets are entirely static — no dynamic parameter inference based on data shape or type","No syntax validation or linting of inserted code; malformed templates are inserted as-is","Requires manual placeholder substitution; no intelligent argument suggestion based on project context","No awareness of existing imports or variable names in the file; may create duplicate imports","Templates use default hyperparameters; no guidance on parameter tuning for specific datasets","No automatic handling of train/test split or cross-validation setup within the template","Does not validate that X and y variables exist or have compatible shapes before insertion","No support for custom model classes or scikit-learn extensions (e.g., imbalanced-learn)","No automatic parameter selection based on data dimensionality or cluster count estimation","Density estimation templates do not include model selection or likelihood evaluation code","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44,"quality":0.28,"ecosystem":0.33,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:36.253Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=scikit-learn-snippets","compare_url":"https://unfragile.ai/compare?artifact=scikit-learn-snippets"}},"signature":"NGo6ATPWr0l7yCkk8WsJ2yrZrZGPeajhddhudPDV0/xNM4/ZutLRQ/qq2aDKZrmIhxrjeD79kbB6rP66b2hzCQ==","signedAt":"2026-06-21T15:04:39.053Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scikit-learn-snippets","artifact":"https://unfragile.ai/scikit-learn-snippets","verify":"https://unfragile.ai/api/v1/verify?slug=scikit-learn-snippets","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}