Skip to main content

Ctrl+K

Welcome to sensAI!

Models with Modular Data Pipelines
Notebook Tutorials
- Introduction to sensAI: Supervised Learning with VectorModels
- Neural Networks
API Reference
- catboost
- columngen
- distance_metric
- feature_importance
- hyperopt
- lightgbm
- local_search
- minizinc
- multi_model
- naive_bayes
- nearest_neighbors
- normalisation
- sklearn_quantile
- tensor_model
- vector_model
- vectoriser
- xgboost
- clustering
- data
  - dataset
  - io_data
- data_transformation
- ensemble
  - ensemble_base
  - models
- evaluation
- feature_selection
  - rfe
- featuregen
  - feature_generator
  - feature_generator_registry
- geoanalytics
- pytorch_lightning
  - pl_models
- sklearn
- torch
  - torch_base
  - torch_data
  - torch_enums
  - torch_eval_util
  - torch_modules
  - torch_opt
  - torchtext
  - torch_models
    
    lstnet
    
    mlp
    
    residualffn
    
    seq
- tracking
- util
  - aggregation
  - cache
  - cache_azure
  - cache_mysql
  - datastruct
  - deprecation
  - dtype
  - git
  - hash
  - helper
  - io
  - jscode
  - logging
  - math
  - multiprocessing
  - pandas
  - pickle
  - plot
  - profiling
  - sequences
  - string
  - tensorboard
  - test
  - time
  - typing
  - version

.rst

evaluation

evaluation#

eval_util
evaluator_clustering
result_set
evaluator
crossval
metric_computation
eval_stats

previous

models

next

crossval

By sensAI contributors

© Copyright 2019-2024 by sensAI contributors.