Lazy Predict Python | Auto-ML | Machine Learning | Data Science | Python Learning
In this tutorial, we will discuss about LazyPredict automatically scores data with a bunch of different models allowing the user to see performance across a variety of methods. I think this is where things are headed.
notebook link
https://github.com/goodluck08/Machine...
Here is more information about this method: https://pypi.org/project/lazypredict/
The first step is to install it. You can go to Anaconda, click on the environment, and then open the terminal using the "play button" next to the environment you want to install it in. Then you can enter the command:
pip install lazypredict
LazyPredict can be a good choice for several use cases where you need to quickly build and evaluate machine learning models with minimal code, such as:
Exploratory data analysis: LazyPredict can be used to quickly explore and analyze your datasets by training and testing multiple models on the data and getting a summary of their performance metrics.
Model selection and tuning: LazyPredict can help you compare and select the best machine learning model for your task by testing multiple models and comparing their performance metrics.
Rapid prototyping: LazyPredict can be used for rapid prototyping of machine learning models, where you want to quickly test the feasibility of a machine learning solution for a problem.
Educational purposes: LazyPredict can be a useful tool for teaching and learning machine learning concepts and techniques, as it allows you to experiment with different models and algorithms without having to write a lot of code.
However, LazyPredict may not be suitable for more complex machine learning tasks where you need more control over the model training and evaluation process, such as:
Large datasets: LazyPredict may not be able to handle large datasets efficiently, as it trains and tests multiple models on the data, which can be computationally expensive.
Custom models: LazyPredict may not support all types of machine learning models and algorithms, so if you need to use a custom or specialized model, you may need to implement it manually.
Fine-tuning models: LazyPredict may not provide fine-grained control over the model training and
hyperparameter tuning process, so if you need to optimize a model for specific performance metrics, you may need to do it manually.
Anybody Can Code
