Model comparison involves analyzing and comparing the performance and behavior of ML models.
ELI5 is a Python library. It is designed to provide explanations for ML models in a human-readable format. It helps users understand how models make predictions. It is done by visualizing feature importances, model coefficients, and other relevant information.
In the context of model comparison, ELI5 can be used to:
- Visualize feature importance
- Explain individual predictions
- Compare models
- Debug models
- Interpret black-box models
tensorflow:
- It is an open-source deep learning framework developed by Google.
- It is used for constructing and schooling neural networks.
- With TensorFlow, you can train complex models tailored to your specific problem domain.
tensorflowby tensorflow
An Open Source Machine Learning Framework for Everyone
tensorflowby tensorflow
C++ 175562 Version:v2.13.0-rc1 License: Permissive (Apache-2.0)
pytorch:
- It is a deep learning framework developed by Facebook's AI Research lab.
- It is thought for its dynamic computation graph and simplicity of use.
- It allows users to analyze and compare model predictions using Eli5's explanation tools.
pytorchby pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
pytorchby pytorch
Python 67874 Version:v2.0.1 License: Others (Non-SPDX)
keras:
- It is a high-stage neural networks API that runs on the pinnacle of TensorFlow.
- It affords an interface for constructing neural networks.
- It integrates with TensorFlow, one of the greatest famous deep getting-to-know frameworks.
scikit-learn:
- It is a comprehensive library for machine learning in Python.
- It provides implementations for various algorithms and tools for model evaluation.
- It enables users to analyze and compare the predictions of these models.
scikit-learnby scikit-learn
scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)
xgboost:
- It is a good and scalable gradient-boosting library.
- It is used for classification and regression tasks.
- scikit-learn provides a consistent API.
xgboostby dmlc
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
xgboostby dmlc
C++ 24228 Version:v1.7.5 License: Permissive (Apache-2.0)
LightGBM:
- LightGBM is a gradient-boosting framework advanced with the aid of Microsoft.
- It is a gradient-boosting framework known for its speed and efficiency.
- It allows users to analyze and compare their predictions using various explanation techniques.
LightGBMby microsoft
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
LightGBMby microsoft
C++ 15042 Version:v3.3.5 License: Permissive (MIT)
lime:
- It is a powerful tool for model interpretation and comparison.
- It facilitates model comparison by providing interpretable insights into predictions.
- It provides human-interpretable explanations for model predictions.
limeby marcotcr
Lime: Explaining the predictions of any machine learning classifier
limeby marcotcr
JavaScript 10684 Version:0.2.0.0 License: Permissive (BSD-2-Clause)
tpot:
- It is an automatic ML device that optimizes ML pipelines. It is done through genetic programming.
- TPOT often incorporates ensemble methods, such as stacking or blending.
- It uses cross-validation to estimate the performance of each pipeline.
tpotby EpistasisLab
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
tpotby EpistasisLab
Python 9085 Version:v0.11.7 License: Weak Copyleft (LGPL-3.0)
yellowbrick:
- It is a Python library that provides visual diagnostic tools for machine learning.
- Yellowbrick provides tools for visualizing model comparison.
- It includes tools for model selection, such as validation curves and learning curves.
yellowbrickby DistrictDataLabs
Visual analysis and diagnostic tools to facilitate machine learning model selection.
yellowbrickby DistrictDataLabs
Python 4016 Version:v1.5 License: Permissive (Apache-2.0)
scikit-plot:
- It offers a variety of plotting functions for visualizing model evaluation metrics.
- It is integrated with scikit-learn, which is a popular machine-learning library.
- It includes functions for comparing many models' performance metrics side by side.
scikit-plotby reiinakano
An intuitive library to add plotting functionality to scikit-learn objects.
scikit-plotby reiinakano
Python 2290 Version:v0.3.7 License: Permissive (MIT)
auto-sklearn:
- It is an AutoML tool that can be beneficial in model comparison tasks.
- It considers a wide range of ML models, preprocessing techniques, and hyperparameter configurations.
- It allows users to analyze and compare the explanations of different models generated.
auto-sklearnby automl
Automated Machine Learning with scikit-learn
auto-sklearnby automl
Python 6984 Version:v0.15.0 License: Permissive (BSD-3-Clause)
FAQ
1. What is Eli5?
Eli5 is a Python library that provides tools for explaining machine learning models. It helps users understand the inner workings of models. It includes feature importance, prediction explanations, and model comparisons.
2. How can Eli5 help in comparing machine learning models?
Eli5 offers various tools to compare machine learning models. Those models feature important analysis, permutation importance, and visualization of model predictions. These tools enable users to assess the strengths and weaknesses of different models. It is also used to make informed decisions.
3. How does Eli5 calculate feature importance?
Eli5 calculates feature importance using techniques like permutation importance. It measures the change in model performance when the values of a feature are shuffled. Features causing a significant drop in performance when shuffled are considered more important.
4. Can Eli5 be used with any machine learning framework?
Yes, Eli5 is compatible with various machine-learning frameworks in Python. It includes scikit-learn, XGBoost, LightGBM, and more. It provides a consistent interface. It is used for model explanation regardless of the underlying framework used.
5. Does Eli5 support only traditional machine learning models, or can it be used with deep learning models as well?
While Eli5 is focused on traditional ML models, it can also be used with deep learning models. But the interpretability of deep learning models is more challenging. Eli5's capabilities may be limited compared to traditional models.