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data-mining

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gensim
jameslamb
jameslamb commented Sep 29, 2019

One unit test in the R package is currently broken. Steps to reproduce on Mac

export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8
Rscript build_r.R
cd R-package/tests
Rscript testthat.R

This results in the following error at the ends of the logs

[LightGBM] [Info] Saving data to binary file /var/folders/xq/wktq4zdx4jd3qdpk34d28m940000gn/T//RtmpiY1DzV/lgb.Dataset_1555
ferret
gyy52380
gyy52380 commented Oct 13, 2019

Describe the bug
When using the cdp driver, during closing of a browser page, this error sometimes appears.

{"level":"warn","time":"x","url":"x","error":"rpcc: the connection is closing","time":"x","message":"failed to close browser page"}
{"level":"error","time":"x","error":": rpcc: the connection is closing: session: detach timed out for session 5C391DF4E758E985AE3CBAA03774E562","t
rasbt
rasbt commented Sep 20, 2019

Currently, we have a "drop_last_proba" parameter, which drops the last "probability" column in the feature set if it is set to True, because it is redundant: p(y_c) = 1 - p(y_1) + p(y_2) + ... + p(y_{c-1}). This can be useful for meta-classifiers that are sensitive to perfectly collinear features.

As mentioned by @bmreiniger in #527 , it might be useful to be able to choose whether the fi

Henlam
Henlam commented Jul 2, 2019

Hello,

first and foremost, thank you for building this wrapper it is of great use for me and many others.

I have question regarding the evaluation:
Most outlier detection evaluation settings work by setting the ranking number n equal the number of outliers (aka contamination) and so did I in my experiments.

My thought concerning the ROC and AUC score was:

  1. Don't we have to to rank th
BlazZupan
BlazZupan commented Oct 14, 2019

The problem
Select Random Features preprocessor as implemented in the Preprocess widget allows the user to enter only a very limited number of attributes. I believe 99 is the current limit. The number of features on the input is usually larger. Say, in image embedding, we have thousands of features. The limit of 99 is therefore too stringent.

Solution
Remove any limit on the number of

annoviko
annoviko commented Jan 25, 2019

Introduction
In case of complex and big data, it is not desirable to search using step=1, for the initial rough search it is much better to be able to search using a bigger step.

Description
The additional parameter should be introduced to the algorithm - step.

  • Additional parameter for the Python implementation.
  • Additional parameter for the C/C++ implementation.
  • U
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