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1500 questions
27
votes
4 answers
macro average and weighted average meaning in classification_report
I use the "classification_report" from from sklearn.metrics import classification_report in order to evaluate the imbalanced binary classification
Classification Report :
precision recall f1-score support
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user10296606
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27
votes
5 answers
BERT vs Word2VEC: Is bert disambiguating the meaning of the word vector?
Word2vec:
Word2vec provides a vector for each token/word and those vectors encode the meaning of the word. Although those vectors are not human interpretable, the meaning of the vectors are understandable/interpretable by comparing with other…
sovon
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27
votes
4 answers
Cross validation Vs. Train Validate Test
I have a doubt regarding the cross validation approach and train-validation-test approach.
I was told that I can split a dataset into 3 parts:
Train: we train the model.
Validation: we validate and adjust model parameters.
Test: never seen before…
NaveganTeX
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27
votes
2 answers
What is the difference between semantic segmentation, object detection and instance segmentation?
I'm fairly new at computer vision and I've read an explanation at a medium post, however it still isn't clear for me how they truly differ.
Guilherme Marques
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27
votes
4 answers
Word2Vec for Named Entity Recognition
I'm looking to use google's word2vec implementation to build a named entity recognition system. I've heard that recursive neural nets with back propagation through structure are well suited for named entity recognition tasks, but I've been unable…
Madison May
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27
votes
2 answers
What is the advantage of using log softmax instead of softmax?
Are there any advantages to using log softmax over softmax? What are the reasons to choose one over the other?
rawwar
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27
votes
1 answer
Adaboost vs Gradient Boosting
How is AdaBoost different from a Gradient Boosting algorithm since both of them use a Boosting technique?
I could not figure out actual difference between these both algorithms from a theory point of view.
CodeMaster GoGo
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27
votes
2 answers
How to deal with time series which change in seasonality or other patterns?
Background
I'm working on a time series data set of energy meter readings. The length of the series varies by meter - for some I have several years, others only a few months, etc. Many display significant seasonality, and often multiple layers -…
Jo Douglass
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27
votes
2 answers
local minima vs saddle points in deep learning
I heard Andrew Ng (in a video I unfortunately can't find anymore) talk about how the understanding of local minima in deep learning problems has changed in the sense that they are now regarded as less problematic because in high-dimensional spaces…
oW_
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27
votes
3 answers
How to sum values grouped by two columns in pandas
I have a Pandas DataFrame like this:
df = pd.DataFrame({
'Date': ['2017-1-1', '2017-1-1', '2017-1-2', '2017-1-2', '2017-1-3'],
'Groups': ['one', 'one', 'one', 'two', 'two'],
'data': range(1, 6)})
Date Groups data
0 …
Kevin
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27
votes
3 answers
How to deal with string labels in multi-class classification with keras?
I am newbie on machine learning and keras and now working a multi-class image classification problem using keras. The input is tagged image. After some pre-processing, the training data is represented in Python list as:
[["dog",…
Dracarys
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27
votes
4 answers
Is there a straightforward way to run pandas.DataFrame.isin in parallel?
I have a modeling and scoring program that makes heavy use of the DataFrame.isin function of pandas, searching through lists of facebook "like" records of individual users for each of a few thousand specific pages. This is the most time-consuming…
Therriault
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27
votes
1 answer
PyTorch vs. Tensorflow Fold
Both PyTorch and Tensorflow Fold are deep learning frameworks meant to deal with situations where the input data has non-uniform length or dimensions (that is, situations where dynamic graphs are useful or needed).
I would like to know how they…
noe
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27
votes
3 answers
Encoding categorical variables using likelihood estimation
I am trying to understand how I can encode categorical variables using likelihood estimation, but have had little success so far.
Any suggestions would be greatly appreciated.
small dwarf
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26
votes
2 answers
Text categorization: combining different kind of features
The problem I am tackling is categorizing short texts into multiple classes. My current approach is to use tf-idf weighted term frequencies and learn a simple linear classifier (logistic regression). This works reasonably well (around 90% macro F-1…
elmille
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