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julearn documentation
  • 1. Getting started
  • 2. What you really need to know
    • 2.1. Why cross validation?
    • 2.2. Data
    • 2.3. Model Building
    • 2.4. Model evaluation
    • 2.5. Model comparison
  • 3. Selected deeper topics
    • 3.1. Applying preprocessing to the target
    • 3.2. Cross-validation consistent Confound Removal
    • 3.3. Hyperparameter Tuning
    • 3.4. Inspecting Models
    • 3.5. Cross-validation splitters
    • 3.6. Stacking Models
    • 3.7. Connectome-based Predictive Modeling (CBPM)
  • 4. Overview of available Pipeline Steps
  • 5. Examples
    • 5.1. Starting with julearn
      • Working with pandas
      • Simple Binary Classification
      • Grouped CV.
      • Multiclass Classification.
      • Stratified K-fold CV for regression analysis
      • Regression Analysis
    • 5.2. Model Comparison
      • Simple Model Comparison
    • 5.3. Inspection
      • Inspecting the fold-wise predictions
      • Inspecting Random Forest models
      • Inspecting SVM models.
      • Preprocessing with variance threshold, zscore and PCA
    • 5.4. Complex Models
      • Transforming target variable with z-score.
      • Tuning Multiple Hyperparameters Grids
      • Stacking Classification
      • Tuning Hyperparameters
      • Regression Analysis
    • 5.5. Confounds
      • Return Confounds in Confound Removal
      • Confound Removal (model comparison)
    • 5.6. Customization
      • Custom Scoring Function for Regression
  • 6. Reference
    • 6.1. Main API
      • julearn.run_cross_validation
    • 6.2. Pipeline
      • julearn.PipelineCreator
      • julearn.TargetPipelineCreator
      • julearn.pipeline.JuTargetPipeline
      • julearn.pipeline.pipeline_creator.Step
    • 6.3. Model Selection
      • julearn.model_selection.ContinuousStratifiedKFold
      • julearn.model_selection.RepeatedContinuousStratifiedKFold
      • julearn.model_selection.ContinuousStratifiedGroupKFold
      • julearn.model_selection.RepeatedContinuousStratifiedGroupKFold
      • julearn.model_selection.StratifiedBootstrap
      • julearn.model_selection.get_searcher
      • julearn.model_selection.list_searchers
      • julearn.model_selection.register_searcher
      • julearn.model_selection.reset_searcher_register
    • 6.4. Base
      • julearn.base.JuBaseEstimator
      • julearn.base.JuTransformer
      • julearn.base.WrapModel
      • julearn.base.ColumnTypes
      • julearn.base.ColumnTypesLike
      • julearn.base.change_column_type
      • julearn.base.get_column_type
      • julearn.base.make_type_selector
      • julearn.base.ensure_column_types
    • 6.5. Inspect
      • julearn.inspect.Inspector
      • julearn.inspect.FoldsInspector
      • julearn.inspect.preprocess
    • 6.6. Models
      • julearn.models.list_models
      • julearn.models.get_model
      • julearn.models.register_model
      • julearn.models.reset_model_register
    • 6.7. Dynamic Selection (DESLib)
      • julearn.models.dynamic.DynamicSelection
    • 6.8. Scoring
      • julearn.scoring.get_scorer
      • julearn.scoring.list_scorers
      • julearn.scoring.register_scorer
      • julearn.scoring.reset_scorer_register
      • julearn.scoring.check_scoring
    • 6.9. Scoring Metrics
      • julearn.scoring.metrics.r_corr
      • julearn.scoring.metrics.r2_corr
    • 6.10. Transformers
      • julearn.transformers.DropColumns
      • julearn.transformers.ChangeColumnTypes
      • julearn.transformers.SetColumnTypes
      • julearn.transformers.FilterColumns
      • julearn.transformers.CBPM
      • julearn.transformers.JuColumnTransformer
      • julearn.transformers.confound_remover.ConfoundRemover
      • julearn.transformers.list_transformers
      • julearn.transformers.get_transformer
      • julearn.transformers.register_transformer
      • julearn.transformers.reset_transformer_register
    • 6.11. Target Transformers
      • julearn.transformers.target.JuTransformedTargetModel
      • julearn.transformers.target.JuTargetTransformer
      • julearn.transformers.target.TargetConfoundRemover
      • julearn.transformers.target.TransformedTargetWarning
      • julearn.transformers.target.get_target_transformer
      • julearn.transformers.target.list_target_transformers
      • julearn.transformers.target.register_target_transformer
      • julearn.transformers.target.reset_target_transformer_register
    • 6.12. Utils
      • julearn.utils.logger
      • julearn.utils.configure_logging
      • julearn.utils.raise_error
      • julearn.utils.warn_with_log
    • 6.13. Typing
      • julearn.utils.typing.JuEstimatorLike
      • julearn.utils.typing.EstimatorLike
      • julearn.utils.typing.EstimatorLikeFit1
      • julearn.utils.typing.EstimatorLikeFit2
      • julearn.utils.typing.EstimatorLikeFity
    • 6.14. Prepare
      • julearn.prepare.prepare_input_data
      • julearn.prepare.check_consistency
    • 6.15. Stats
      • julearn.stats.corrected_ttest
    • 6.16. Vizualization
      • julearn.viz.plot_scores
  • 7. Configuring julearn
  • 8. Contributing to julearn
  • 9. Maintaining
  • 10. Frequenly Asked Questions
  • 11. What’s new
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5.1. Starting with julearn#

Examples showing how to use basic julearn functionality.

Working with pandas

Working with pandas

Simple Binary Classification

Simple Binary Classification

Grouped CV.

Grouped CV.

Multiclass Classification.

Multiclass Classification.

Stratified K-fold CV for regression analysis

Stratified K-fold CV for regression analysis

Regression Analysis

Regression Analysis

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5. Examples
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