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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

2
votes
I am told both regression coefficients cannot exceed 1 Well, yes they can. There are no constraints about values of coefficients. Where did you get it from? The slope is simply the tangent of … the angle between your line and OX axis, so it can get any value in $\mathbb{R}$ The only trivial "constraint" is that given simple linear regression from $\mathbb{R}$ to $\mathbb{R}$, if you look at …
answered Aug 31 '13 by lejlot
2
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There is a sklearn library in python, which (among others) implements Ordinary Least Squares Linear Regression http://scikit-learn.org/stable/modules/generated … /sklearn.linear_model.LinearRegression.html Sample usage: from sklearn import linear_model #creating a regression object regr = linear_model.LinearRegression() #runnin OLS on your data, assuming that you already have arrays x and y regr.fit( x, y ) #displaying coefficients matrix print regr.coef_ …
answered Aug 27 '13 by lejlot
2
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Many models can actually provide you with the uncertainty measure, first of all: Naive Bayes directly models the P(y|x) probability, which is exactly what you are asking for Support Vector Machine d …
answered Aug 7 '13 by lejlot
1
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One possible approach which is statistically sound is to build an ensemble of models using only subset of features (random) in each simple one. For example consider building something like random fore …
answered Aug 14 '15 by lejlot
3
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I learned that in supervised learning, the result of regression is often a real number This is a false statement. There is no such "rule", regression is not limited to $\mathbb{R}$, even linear … regression is defined for $\mathbb{R}^m$ for any $m \in \mathbb{N_+}$. What is even more important, even for $m=1$, the result of regression is not a number but rather - parameters of your model …
answered Aug 30 '13 by lejlot
1
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If you want an "online learning algorithm", which will try to improve itself during some kind of environmental simulation instead of preparing training data in form of $(x_i,y_i)$, then this looks lik …
answered Aug 28 '13 by lejlot
3
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In general, dealing with missing input values is always problematic. To my best knowledge, none of the existing methods can deal with it without introducing some bias to the model, so you have to cons …
answered Aug 12 '13 by lejlot