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I would like to know if there is an implementation of hierarchical classification in the scikit-learn package or in any other python package.
Thank you so much in advance.
I couldn't find an implementation of Hierarchical Classification on scikit-learn official documentation. But I found this repository recently. This module is based on scikit-learn's interfaces and conventions. I hope this will be useful.
https://github.com/globality-corp/sklearn-hierarchical-classification
The globality-corp repo was deleted, but I found this fork, so I am posting it:
https://github.com/fbenites/sklearn-hierarchical-classification
As above, it is a module following the scikit-learn's conventions and is used along with sklearn classifiers.
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I am working on a project to use a pre-trained model and finetune it for customized language translations, for example from English to French. Is it possible to load these models in Tensorflow and run them to see how translations turn out and fine-tune afterward?
Probably the fastest way to do so is relying on the HuggingFace transformers library. If you're not familiar with it, you may take a look at their official documentation. To fine-tune a BART for NMT you can use directly this provided script (it works with some other pre-trained models too).
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I am looking for beginner Machine Learning Linear Regression problems. I searched in Kaggle, but couldn't find a proper one. Can you please suggest me a beginner problem from Kaggle, or from any other platform?
Thanks in advance.
Kaggle has tons of linear regression notebooks and datasets to learn from, most popular ones are probably about house pricing (given certain house features predict it's price).
Here's a new one I'm looking forward to solve:
Ben & Jerry's flavours and ratings ---> products.csv
The main goal would be predict wich ice cream flavours are better accepted based on it's ingredients.
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Hey I want to use word2vec algorithm without implementing it (I saw a lot of places that teaches how to implement one).
does anyone can tell me what is the best lib to use?
I saw there is Genesim, and Deeplearning4j. also TensorFlow but I can't find a place where they have the function that I need (only how to implement with this lib).
can someone give some comparison about efficiency? how easy to use? the word2vec algorithm for each lib?
any helpful tip or resources would be great.
SpaCy comes with pretrained word embeddings that you can use - it's very easy to use, you can find examples on how to download the embedding and use it here.
The implementation in the Python gensim library offers a Word2Vec model class that is flexible (with options not available elsewhere) & as fast as the original word2vec.c code released by the Google researchers who created the algorithm.
You can see its docs at:
https://radimrehurek.com/gensim/models/word2vec.html
There's an intro tutorial that can be run as in a 'Jupyter' notebook:
https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html
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My product is made in Python and I need Semantic Analysis for classification of sentences into questions, complaints, etc. Which is the best framework for the same?
I think the best approach would be, try several methods, cross-validated each method using a separate validation data set (or using K-Fold cross-validation) and pick the best one.
So as a starting point you can try:
Simple Logistic Regression using scikit-learn
Random Forest or Gradient Boosting Tree
Recurrent Neural Networks using Keras library.
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Been tinkering with OpenCV in python. The face detection demo is impressive.
Are there any free collection of Haar classifiers aside from the face, eyes and full body ones?
Sure, there are a few common ones which show up in most people's projects, although opencv should come with these:
cv haar cascades
eye cascades
You can make your own cascades, but be warned, it's a pretty cumbersome process. The best tutorial I found was this one.
HTH
Alejandro F. Reimondo has a few cascades on his site: http://alereimondo.no-ip.org/OpenCV/34