There are several tools that are commonly used for machine learning on tabular data, and the best one for you will depend on your specific use case and requirements. Here are a few popular options:
- Scikit-learn: This is a widely-used library for machine learning in Python, and it includes a variety of algorithms for classification, regression, and clustering, as well as tools for preprocessing, model evaluation, and model selection. It is relatively easy to use and has a large and active community, which makes it a great choice for many applications.
- XGBoost: This is a powerful and efficient library for gradient boosting, which is a type of ensemble learning that can be used for both classification and regression. XGBoost has been shown to perform well in many machine learning competitions and is often used in industry as well.
- LightGBM: Similar to XGBoost and it’s a gradient-boosting framework that uses a tree-based learning algorithm. LightGBM is designed to be more efficient than XGBoost in terms of both memory usage and training time. It has a faster training time and can handle large datasets and also it is capable of handling categorical variables as well.
- CatBoost: It’s another gradient-boosting framework developed by Yandex with the main focus on handling categorical variables. it is designed to perform well on datasets with a large number of categorical features and it also has built-in handling of missing values.
- Pandas: This is a popular data manipulation library in Python, and it provides tools for data cleaning, wrangling, and exploration. It’s great when you need to do some preprocessing of your data before applying a machine-learning algorithm.
- TensorFlow and PyTorch: These are popular libraries for deep learning, and they can be used for a wide range of tasks, including image classification, natural language processing, and speech recognition.
All of these tools are widely used and have their own strengths and weaknesses, so it’s worth experimenting with a few different options to see which one works best for your specific task.
Also, many other libraries and frameworks available like Keras, H2O.ai and etc. but it depends on your use case and what you’re comfortable with.