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pycaret | An open-source , low-code machine learning library in Python | Machine Learning library

 by   pycaret Jupyter Notebook Version: 3.0.0rc6 License: MIT

 by   pycaret Jupyter Notebook Version: 3.0.0rc6 License: MIT

kandi X-RAY | pycaret Summary

pycaret is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. pycaret has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. 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 lines 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, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.
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Quality
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License
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kandi-support Support

  • pycaret has a medium active ecosystem.
  • It has 6843 star(s) with 1556 fork(s). There are 127 watchers for this library.
  • There were 3 major release(s) in the last 6 months.
  • There are 260 open issues and 1651 have been closed. On average issues are closed in 49 days. There are 6 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of pycaret is 3.0.0rc6
pycaret Support
Best in #Machine Learning
Average in #Machine Learning
pycaret Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • pycaret has 0 bugs and 0 code smells.
pycaret Quality
Best in #Machine Learning
Average in #Machine Learning
pycaret Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • pycaret has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • pycaret code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
pycaret Security
Best in #Machine Learning
Average in #Machine Learning
pycaret Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • pycaret is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
pycaret License
Best in #Machine Learning
Average in #Machine Learning
pycaret License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • pycaret releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
  • It has 23953 lines of code, 781 functions and 89 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
pycaret Reuse
Best in #Machine Learning
Average in #Machine Learning
pycaret Reuse
Best in #Machine Learning
Average in #Machine Learning
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pycaret Key Features

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

pycaret Examples and Code Snippets

See all related Code Snippets

Community Discussions

Trending Discussions on pycaret
  • is test data used in Pycaret time series(beta) completely unseen by the model(s)?
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  • Pycaret - 'Make_Time_Features' object has no attribute 'list_of_features'
  • Pycaret anomaly detection setup: ValueError: Setting a random_state has no effect since shuffle is False
  • Weird Time-Series Graph Using Pycaret and plotly
  • Creating 12 month MA in PyCaret time series gives a column with NA values
  • What does the Anomaly_Score in Pycaret.anomaly library mean?
  • How to install PyCaret in AWS Glue
  • Unable to install sklearn 0.23.2 with pip 20.2.4 and python 3.9
  • Pycaret.regression.compare_models: Evaluation table neither returned nor shown
Trending Discussions on pycaret

QUESTION

is test data used in Pycaret time series(beta) completely unseen by the model(s)?

Asked 2022-Apr-04 at 10:51

Post checking official documentation and example, I am still confused if test data passed to the setup function is completely unseen by the model???

from pycaret.datasets import get_data
from pycaret.internal.pycaret_experiment import TimeSeriesExperiment

# get data
y = get_data('airline', verbose=False)

# no of future steps to forecast
fh = 12 # or alternately fh = np.arange(1,13)
fold = 3

# setup
exp = TimeSeriesExperiment()
exp.setup(data=y, fh=fh, fold = fold)
exp.models()

which gives description as:

enter image description here

Also, checked at cv-graph, we can conclude that test data set is not used while cv. But, Still as it's not mentioned anywhere about it, need a concrete evidence.

Train-Test split enter image description here

Train c-v splits enter image description here

ANSWER

Answered 2022-Apr-04 at 10:51

If you notice the cv splits, they do not use the test data at all. So any step such as create_model, tune_model, blend_model, compare_models that use Cross-Validation, will not use the test data at all for training.

Once you are happy with the models from these steps, you can finalize the model using finalize_model. In this case, whatever model you pass to finalize_model is trained on the complete dataset (train + test) so that you can make true future predictions.

Source https://stackoverflow.com/questions/71331098

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pycaret

PyCaret's default installation only installs hard dependencies as listed in the requirements.txt file.

Support

With PyCaret >= 2.2, you can train models on GPU and speed up your workflow by 10x. To train models on GPU simply pass use_gpu = True in the setup function. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default version or the full version. As of the latest release, the following models can be trained on GPU:.

Find more information at:

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Install
  • pip install pycaret

Clone
  • https://github.com/pycaret/pycaret.git

  • gh repo clone pycaret/pycaret

  • git@github.com:pycaret/pycaret.git

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