Questions tagged [machine-learning]
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
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Train/Test/Validation Set Splitting in Sklearn
How could I randomly split a data matrix and the corresponding label vector into a X_train, X_test, ...
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How to draw Deep learning network architecture diagrams?
I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
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What is the "dying ReLU" problem in neural networks?
Referring to the Stanford course notes on Convolutional Neural Networks for Visual Recognition, a paragraph says:
"Unfortunately, ReLU units can be fragile during training and can
"die". For ...
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How do you visualize neural network architectures?
When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture.
What are good / simple ways to visualize common ...
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The cross-entropy error function in neural networks
In the MNIST For ML Beginners they define cross-entropy as
$$H_{y'} (y) := - \sum_{i} y_{i}' \log (y_i)$$
$y_i$ is the predicted probability value for class $i$ and $y_i'$ is the true probability ...
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Best python library for neural networks
I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?
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Python vs R for machine learning
I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people using Python.
What ...
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Choosing a learning rate
I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...
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Why do cost functions use the square error?
I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable.
I have learnt that there is a hypothesis, which is:
$h_\theta(x)=\...
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When should I use Gini Impurity as opposed to Information Gain (Entropy)?
Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)?
Which metric is better to use in different scenarios while using decision trees?
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Advantages of AUC vs standard accuracy
I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...
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In supervised learning, why is it bad to have correlated features?
I read somewhere that if we have features that are too correlated, we have to remove one, as this may worsen the model. It is clear that correlated features means that they bring the same information, ...
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strings as features in decision tree/random forest
I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-...
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Data scientist vs machine learning engineer
What are the differences, if any, between a "data scientist" and a "machine learning engineer"?
Over the past year or so "machine learning engineer" has started to show up a lot in job postings. ...
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GBM vs XGBOOST? Key differences?
I am trying to understand the key differences between GBM and XGBOOST. I tried to google it, but could not find any good answers explaining the differences between the two algorithms and why xgboost ...
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