Mljar-examples Alternatives

Similar projects and alternatives to mljar-examples based on common topics and language

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www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better mljar-examples alternative or higher similarity.

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mljar-examples reviews and mentions

Posts with mentions or reviews of mljar-examples. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-05.

MLJAR Automated Machine Learning for Tabular Data (Stacking, Golden Features, Explanations, and AutoDoc)
3 projects | /r/learnmachinelearning | 5 Jan 2021

All ML experiments have automatic documentation that creates Markdown reports ready to commit to the repo (example1, example2).
Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
3 projects | news.ycombinator.com | 5 Jan 2021

The creator here. I'm working on AutoML since 2016. I think that latest release (0.7.15) of MLJAR AutoML is amazing. It has ton of fantastic features that I always want to have in AutoML:
- Operates in three modes: Explain, Perform, Compete.
- `Explain` is for data exploratory and checking the default performance (without HP tuning). It has Automatic Exploratory Data Analysis.
- `Perform` is for building production-ready models (HP tuning + ensembling).
- `Compete` is for solving ML competitions in limited time amount (HP tuning + ensembling + stacking).
- All ML experiments have automatic documentation which creates Markdown reports ready to commit to the repo ([example](https://github.com/mljar/mljar-examples/tree/master/Income_c...)).
- The package produces extensive explanations: decision tree visualization, feature importance, SHAP explanations, advanced metrics values.
- It has advanced feature engineering, like: Golden Features, Features Selection, Time and Text Transformations, Categoricals handling with target, label, or one-hot encodings.