Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. (by facebook)
scikit-learn
scikit-learn: machine learning in Python (by scikit-learn)
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| Prophet | scikit-learn | |
|---|---|---|
| 88 | 37 | |
| 14,431 | 50,105 | |
| 1.4% | 1.3% | |
| 6.5 | 9.9 | |
| 17 days ago | 1 day ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Prophet
Posts with mentions or reviews of Prophet.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-06.
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Reverse Engineering Video Game Stock Prices
Prophet is a forecasting model made by Facebook.
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ARIMA models are solid though
I like FB Prophet and LinkedIn's GreyKite
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LSTM/CNN architectures for time series forecasting[Discussion]
Prophet
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Basic Time Series Prediction
I like Meta's Prophet. It's a flexible model that I feel is easy to understand and explain to non-technical stakeholders. Implementations for R and python. It can be as simple as a univariate series, or accept exogenous explanatory variables (categorical & continuous). Seasonality is part of the model, out of the box. It also has decent default parameters if you're not looking to do a lot of tuning. Lastly, the documentation is quite thorough and approachable. https://facebook.github.io/prophet/
- prophet: NEW Data - star count:14301.0
scikit-learn
Posts with mentions or reviews of scikit-learn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-08.
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Will it benefit me having a portfolio alongside my cv?
I say 'usually' because it depends on what you're referring to as 'coding'. From what you're describing, it seems that you want to be able to take data, clean it up and perform a whole bunch of analysis/inferences on it. In that case, I think the coding skill there would be stuff that allows you to do data manipulation and data clean up (knowledge of R, knowing Python as it pertains to data stuff e.g. scikit learning). Knowing how to build an App would not necessarily be a selling skill
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[D] Looking for a python library that implements decision tree regressors handling categorical features
Perhaps this would be of interest to you: NOCATS
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What to do with some data?
Are you using scikit-learn for your training? If so, you may try running the models on one another. If you're using custom kernels, you may want to use a different set of them for the test set.
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Desmistificando roteirizações com Python
Scikit-learn
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Inside the hood of SciKit-Learn library??? How do I get original codes? what is the magic search word?
In short, you can read through the code, it's open-source. For instance, you can find LogisticRegression here.
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Python for everyone :Mastering Python The Right Way
http://scikit-learn.org/ - Machine learning with Python https://www.tensorflow.org/ - Deep learning with Python https://www.djangoproject.com/ - https://www.python.org/dev/peps/pep-0008
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Roadmap to self-learning AI
My only gripe is that the Labs are in R and not Python, but honestly the [scikit-learn](https://scikit-learn.org/) user guide & docs have been straightforward enough to apply the same knowledge in Python for me with some trial and error.
- scikit-learn test case results?
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How do you reduce information leakage and bias when going from descriptive analytics to prescriptive analytics?
I'd say, the first question you'd need to ask yourself is "Why do I want to do statistical tests" and "what kind of statistical tests do I want to do?". Most of them rely on a bunch of assumptions and just winging it will produce a number that will be reported and used but is terribly wrong. Funnily enough, scikit-learn does not directly give you p-values for this very reason and advise you to run the same regression in statsmodels.
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Learning python, what next?
Machine learning and statistical analysis? http://scikit-learn.org
What are some alternatives?
When comparing Prophet and scikit-learn you can also consider the following projects:
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
xgboost - 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
Surprise - A Python scikit for building and analyzing recommender systems
greykite - A flexible, intuitive and fast forecasting library
darts - A python library for easy manipulation and forecasting of time series.
MLflow - Open source platform for the machine learning lifecycle
gensim - Topic Modelling for Humans
PyBrain
sktime - A unified framework for machine learning with time series