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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.
2
votes
Accepted
How to use multilevel and hashed categorical variables to build an anomaly detection classifier
The core of your question seems to be whether a hash of an IP address can be used as a replacement for the IP address itself or if this will not perfectly differentiate the IP addresses (due to hash c …
2
votes
Accepted
How to find how which inputs correlate to output in XOR way
You have $n$ binary-valued independent variables and $m$ observations in a data matrix $X$ and your dependent variable $y$ is the XOR ($\oplus$) of some subset of the independent variables. Consider a …
23
votes
Accepted
Why f beta score define beta like that?
Letting $\beta$ be the weight in the first definition you provide and $\tilde\beta$ the weight in the second, the two definitions are equivalent when you set $\tilde\beta = \beta^2$, so these two defi …
3
votes
How to compare features and classifiers which achieve perfect accuracy?
There's value in simplicity. Let's assume your logistic regression model trained on feature 7 had intercept $a$ and coefficient $b$ for feature 7. This means the model predicts 1 whenever feature 7 ex …
9
votes
1
answer
3k
views
Variance-covariance matrix for ridge regression with stochastic $\lambda$
In ridge regression with design matrix $X$, outcomes $y$, fixed regularization parameter $\lambda$, and errors $\epsilon\sim\mathcal{N}(0, \sigma^2I)$, the computations for the ridge regression coeffi …
29
votes
For linear classifiers, do larger coefficients imply more important features?
Not at all. The magnitude of the coefficients depends directly on the scales selected for the variables, which is a somewhat arbitrary modeling decision.
To see this, consider a linear regression mod …