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Top Questions
114 votes

Is it possible to train a neural network without backpropagation?

machine-learning neural-networks optimization backpropagation asked Sep 20, 2016 at 1:48
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43 votes

Is LSTM (Long Short-Term Memory) dead?

machine-learning natural-language lstm sequence-analysis asked Jun 18, 2020 at 9:44
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39 votes

Why are survival times assumed to be exponentially distributed?

distributions survival assumptions exponential asked Mar 17, 2017 at 15:10
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34 votes

What exactly is the difference between a parametric and non-parametric model?

machine-learning neural-networks nonparametric terminology parametric asked Mar 20, 2017 at 13:54
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33 votes

Why are symmetric positive definite (SPD) matrices so important?

mathematical-statistics optimization covariance-matrix intuition linear-algebra asked Jul 15, 2016 at 19:13
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33 votes

If I generate a random symmetric matrix, what's the chance it is positive definite?

probability matrix random-generation eigenvalues random-matrix asked Jan 8, 2018 at 18:54
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32 votes

Can degrees of freedom be a non-integer number?

r degrees-of-freedom gam asked May 21, 2017 at 17:00
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30 votes

What are the impacts of choosing different loss functions in classification to approximate 0-1 loss

machine-learning classification optimization loss-functions asked Jul 7, 2016 at 12:58
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29 votes

When should we discretize/bin continuous independent variables/features and when should not?

machine-learning continuous-data feature-construction binning asked Aug 19, 2016 at 17:31
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28 votes

Why there are two different logistic loss formulation / notations?

logistic generalized-linear-model notation loss-functions asked Aug 13, 2016 at 9:10
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Top Answers
58
Regularization methods for logistic regression
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55
Why is 600 out of 1000 more convincing than 6 out of 10?
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43
Is there any intuitive explanation of why logistic regression will not work for perfect separation case? And why adding regularization will fix it?
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38
How could stochastic gradient descent save time compared to standard gradient descent?
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33
Is an overfitted model necessarily useless?
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28
Boosting: why is the learning rate called a regularization parameter?
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28
Are neural networks better than SVMs?
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26
Do all machine learning algorithms separate data linearly?
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26
Is low bias in a sample a synonym for high variance?
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24
Binary classification with strongly unbalanced classes
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23
How does linear base learner works in boosting? And how does it works in the xgboost library?
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23
Cohen's kappa in plain English
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22
Why does feature engineering work ?
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22
Why use gradient descent for linear regression, when a closed-form math solution is available?
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22
How to decide between PCA and logistic regression?
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19
what makes neural networks a nonlinear classification model?
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19
What makes a classifier misclassify data?
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16
How do R and Python complement each other in data science?
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14
How to know if a learning curve from SVM model suffers from bias or variance?
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13
To maximize the chance of correctly guessing the result of a coin flip, should I always choose the most probable outcome?
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13
What are the four axes on PCA biplot?
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12
If I want an interpretable model, are there methods other than Linear Regression?
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12
Error increase on L2 regularization in an NN
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12
Why study convex optimization for theoretical machine learning?
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12
How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?
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12
How to run linear regression in a parallel/distributed way for big data setting?
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11
Can duplicate examples create multi-collinearity?
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11
What I should do if no distribution fits my dataset?
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11
Avoid overfitting in regression: alternatives to regularization
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11
What does it mean for a linear regression to be statistically significant but has very low r squared?
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