Jason Shin
Jason Shin
Now we can upload the library on cdn.js
Hey @FranklinWhale , thanks for reporting this. I will take a look at it soon. Just thinking, we should be able to just remove the optional flag. For the time...
Initial ideas from @greed2411 - crf stuff: https://sklearn-crfsuite.readthedocs.io/en/latest/ - various ensemble models from mlxtend: http://rasbt.github.io/mlxtend/ - gridsearch, randomizedsearch, bayesianoptimizedsearch from https://scikit-optimize.github.io/ - guassian process
@LSBOSS Thanks for this, I think I can have a look at it soon. We need to set a random state to the RF classifier before running the tests.
This is easy to implement now because we have `normalize` API
As a result of initial async refactor merge by @OlegStotsky , we can now implement updates to the doc processor to correctly render sync vs async APIs
Thanks for the suggestion @nsthorat. We agree with you that async API will be necessary because we do care about performance. We are going to do further planning on how...
I found the ML-From-Scratch example and the StatQuest explanation exactly aligns in terms of implementation. I will work on the feature primarily based on the ML-From-Scratch example.
Started the implementation here https://github.com/machinelearnjs/machinelearnjs/tree/feature/adaboost
@benjaminmcdonald suggested refactoring go about `tf.sum(tf.where(tf.equal(y, pred), tf.zerosLike(w), w))` is equivalent to ``` // Sum of weights of misclassified samples // w = [0.213, 0.21342] -> y = [1, 2]...