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Top Questions
92 votes

Is it possible to train a neural network without backpropagation?

machine-learning neural-networks optimization backpropagation asked Sep 20 '16 at 1:48
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36 votes

Why are survival times assumed to be exponentially distributed?

distributions survival assumptions exponential asked Mar 17 '17 at 15:10
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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 '18 at 18:54
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28 votes

Can degrees of freedom be a non-integer number?

r degrees-of-freedom gam asked May 21 '17 at 17:00
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26 votes

What are the classical notations in statistics, linear algebra and machine learning? And what are the connections between these notations?

machine-learning probability self-study optimization asked Jun 4 '16 at 15:34
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26 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 '16 at 12:58
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21 votes

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

mathematical-statistics optimization covariance-matrix intuition linear-algebra asked Jul 15 '16 at 19:13
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21 votes

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

machine-learning continuous-data feature-construction binning asked Aug 19 '16 at 17:31
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21 votes

Why there are two different logistic loss formulation / notations?

logistic generalized-linear-model notation loss-functions asked Aug 13 '16 at 9:10
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20 votes

Is there any intuitive explanation of why logistic regression will not work for perfect separation case? And why adding regularization will fix it?

logistic generalized-linear-model optimization intuition hauck-donner-effect asked Oct 13 '16 at 3:30
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Top Answers
55
Why is 600 out of 1000 more convincing than 6 out of 10?
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48
Regularization methods for logistic regression
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26
Do all machine learning algorithms separate data linearly?
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25
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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25
Is an overfitted model necessarily useless?
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25
Is low bias in a sample a synonym for high variance?
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23
Boosting: why is the learning rate called a regularization parameter?
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21
How could stochastic gradient descent save time comparing to standard gradient descent?
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20
Why does feature engineering work ?
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20
Why use gradient descent for linear regression, when a closed-form math solution is available?
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19
What makes a classifier misclassify data?
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18
what makes neural networks a nonlinear classification model?
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18
How to decide between PCA and logistic regression?
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16
How do R and Python complement each other in data science?
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15
Binary classification with strongly unbalanced classes
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13
How does linear base learner works in boosting? And how does it works in the xgboost library?
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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
How to know if a learning curve from SVM model suffers from bias or variance?
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12
Cohen's kappa in plain English
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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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11
Avoid overfitting in regression: alternatives to regularization
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11
Why study convex optimization for theoretical machine learning?
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11
Error increase on L2 regularization in an NN
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11
What are the four axes on PCA biplot?
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11
What is the equivalent in R of scikit-learn's `LogisticRegression` with `penalty=“l2”`
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11
If I want an interpretable model, are there methods other than Linear Regression?
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9
what does one mean by numerical integration is too expensive?
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9
What makes mean square error so good?
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8
How to run linear regression in a parallel/distributed way for big data setting?
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8
How important is basis expansion for deep nets?
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