Metrics
seqeval
| Metrics | seqeval | |
|---|---|---|
| 2 | 1 | |
| 1,617 | 1,046 | |
| - | 0.6% | |
| 0.0 | 0.0 | |
| over 1 year ago | 11 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
Metrics
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Model evaluation - MAP@K
Starting with Python we’re going to code the functions from scratch using the values determined from the linear regression model. First we’re going to write a function to calculate the Average Precision at K. It will take in three values, the value from the test set, and value from the model prediction, and finally the value for K. This code can be found in the Github for the ml_metrics Python Library.
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How to Judge your Recommendation System Model ?
These metrics are straightforward to implement, also can be obtained from here. Happy Learning !
seqeval
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Beginner questions about NER model evaluation.
. The standard way to evaluate NER (or any other sequence labelling problem) is to use the conlleval script (https://www.clips.uantwerpen.be/conll2000/chunking/output.html) or through the seqeval package in python (https://github.com/chakki-works/seqeval) . Either way, you need a list of predicted labels and a list of gold labels (see the code example in the link, it should be trivial to converse your output to the same data format).
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
scikit-learn - scikit-learn: machine learning in Python
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
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
Keras - Deep Learning for humans
gym - A toolkit for developing and comparing reinforcement learning algorithms.
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.