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Questions tagged [partial-least-squares]

Partial Least Squares (PLS) is a regression techniques used in cases where there is high predictor correlation.

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Measuring features effect and importance in Partial Least Square (PLS) regression

Context: it is possible to assess features importance and effect for a model using model-independent scoring techniques such as Partial Dependence (PD) profile, Acculumated Local Effect (ALE) profile, ...
Paul's user avatar
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Can we automatically chose k value in k-means algorithm?

Can we choose automatically the K value, trying every possible values (k=1,.., n) where n is the number of instances to be clustered. We then keep the value of K for which we obtained the minimum ...
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How effective is Moore Penrose for solving regression problems with overdetermined system of equations?

For regression problems with #Predictors > #observations, I recently read about Moore Penrose (pseudo inverse method) which solves the problem of non invertible matrix in OLS for regression problems. ...
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When using Partial Least Squares (PLS), does the response distribution need to be symmetric?

I've been working through the exercises in the Applied Predictive Modeling book. The solution guide for Chapter 6 exercise 2 states that a log transformation is required to transform the response ...
bxp151's user avatar
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Partial least squares (PLS)

I am relatively new to Orange, trying to utilise it for linear regression, in particular partial least squares (PLS). My statistics knowledge is in the moment not good enough to know whether I could ...
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How exactly dependent variable is expressed in terms of independent variables using Partial Least Square Regression Method?

I understand the working of NIPALS algorithm but while doing the regression using PLS how exactly the relation between known and unknown is established using Principle Component Analysis. The idea is ...
Shaleen Jain's user avatar
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Choosing best methods for estimating the unknown parameters in a linear regression model

Given some dataset for prediction, for eg say I have different housing price prediction dataset: dataset 1 : 100 training and 100 testing sample, 50 feature dataset 2 : 100 training and 100 ...
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