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Results tagged with machine-learning
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user 191003
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
1
vote
Accepted
PCA: Out of Sample Variance Explained
Unless your train and test data follow the exact same distribution, the eigenvectors of your train data are different from test.
Let's assume that train and test distributions are very similar and w …
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1
vote
Accepted
is it valid to use pearson correlation in order to create new features?
In general, using target values isn't a very sound procedure. I'm guessing you'll have to rely on an unsupervised method to generate target values for your test data. So you can imagine that your solu …
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6
votes
How exactly is sparse PCA better than PCA?
To understand the advantages of sparsity in PCA, you need to make sure you know the difference between "loadings" and "variables" (to me these names are somewhat arbitrary, but that's not important). …
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4
votes
I am learning from Pattern Recognition and Machine Learning, Chris Bishop any good resources?
I think an often overlooked book is Information Theory, Inference, and Learning Algorithms by David MacKay.
It follows the general framework of PRML, since the authors seem to have a similar (at leas …
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1
vote
Accepted
Does the value of the target for binary classification matter? If so, how?
Yes, it does -- for some binary classification procedures.
For example, gradient boosting is formulated around the assumption that the labels are -1 or 1 (See "Boosting and Additive Trees" Chapter in …
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0
votes
Accepted
Log-Likelihood in EM Cluster
-15.671233 is better than -52.97762.
The log-likelihood in this case, is the probability of your data given the estimated model parameters. The higher the probability, the better the fit. The reason t …
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1
vote
How to calculate the derivative of crossentropy error function?
An easy way to remember this is to internalize the gradient of the cross-entropy with respect to network parameters, which is famously $t_i - o_i$.
The last slide does this correctly. So, it looks l …
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