mljar-supervised good first issues (Python)

Add support for new data types 16 days ago

enhancement help wanted

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Problem about param ml_task="regression", 24 days ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Incorrect AUC value in CatBoost chart with sample_weight 4 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

leaderboard is showing negative values for f1 metric 4 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

mljar should not configure logging level 4 months ago

enhancement help wanted

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Cross validation makes duplicate samples 5 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Web App generated from AutoML - adjust steps in numeric widgets 5 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

[Request] Allow repeated k-fold cross validation 5 months ago

help wanted good first issue docs

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Choosing "f1" score, the model still considers "accuracy" 5 months ago

bug help wanted

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Get confidence scores for regression predictions 5 months ago

enhancement help wanted

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Google colab - Feature selection not working 5 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

tqdm warning in notebook 5 months ago

bug help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

add feature importance heatmap for all models 5 months ago

enhancement help wanted good first issue

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Precidition for all models produced during traing 12 months ago

enhancement help wanted

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

Compute predictions from selected model about 1 year ago

enhancement help wanted

mljar/mljar-supervised

3.3K

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Python

#automated-machine-learning#automl#automl-api#automl-python#catboost#data-science#decision-tree#ensemble#feature-engineering#hyper-parameters#hyperparameter-optimization#lightgbm#machine-learning#mljar#neural-network#random-forest#scikit-learn#xgboost

mljar/mljar-supervised

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