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Announcing PyCaret: An open source, low-code machine learning library in Python

#artificialintelligence

We are excited to announce PyCaret, an open source machine learning library in Python to train and deploy supervised and unsupervised machine learning models in a low-code environment. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment. In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.


PyCaret: An open source low-code machine learning library in Python MarkTechPost

#artificialintelligence

If you are looking for a Python library to train and deploy supervised and unsupervised machine learning models in a low-code environment, then you should try PyCaret. From data preparation to model deployment, PyCaret allows all these processes in minimum time using your choice of notebook environment. PyCaret enables data scientists and data engineers to perform end-to-end experiments quickly and efficiently. While most of the open-source machine learning libraries require complex lines of codes, PyCaret is a useful low-code library that can increase the performance in complex machine learning tasks with only a few lines of code. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.


Caret Package - A Practical Guide to Machine Learning in R

#artificialintelligence

Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it a decision tree or xgboost, caret helps to find the optimal model in the shortest possible time. Caret nicely integrates all the activities associated with the model development in a streamlined workflow, for nearly every major ML algorithm available in R. Actually we will not just stop with the caret package but will also go a step ahead and see how to smartly ensemble predictions from multiple best models and possibly produce an even better prediction using caretEnsemble. Caret is short for Classification And REgression Training. With R having so many implementations of machine learning algorithms, spread across packages it may be challenging to keep track of which algorithm resides in which package. Sometimes the syntax and the way to implement the algorithm differ across packages combined with preprocessing and looking at the help page for the hyperparameters (parameters that define how the algorithm learns) can make building predictive models an involved task. Well, thanks to caret because no matter which package the algorithm resides, caret will remember that for you and may just prompt you to run install.package Later in this tutorial I will show how to see all the available ML algorithms supported by caret (it's a long list!) and what hyperparameters can be tuned.


A quick introduction to machine learning in R with caret

#artificialintelligence

If you've been using R for a while, and you've been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I'm going to introduce you to an R package: the caret package. We'll build a very simple machine learning model as a way to learn some of caret's basic syntax and functionality. But before diving into caret, let's quickly discuss what machine learning is and why we use it. Machine learning is the study of data-driven, computational methods for making inferences and predictions.


A quick introduction to machine learning in R with caret - SHARP SIGHT LABS

#artificialintelligence

If you've been using R for a while, and you've been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I'm going to introduce you to an R package: the caret package. We'll build a very simple machine learning model as a way to learn some of caret's basic syntax and functionality. But before diving into caret, let's quickly discuss what machine learning is and why we use it. Machine learning is the study of data-driven, computational methods for making inferences and predictions.