Questions tagged [linear-models]
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29 questions
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Please make a distinction between a linear model and a genalized linear model in statistical way?
These concepts seem to be easy but are really difficult to be understood. Please make me understand the idea in a non-technical way.
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Which is best model for dataset with binary variables and time between some variables
I have data that has 20 variables, all of which are binary 1/0 values
I have a result(days) which is an integer value.
The overview of this data is that is we are trying to model which of the 20 x ...
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What is the impact of low correlation on regression and classification problems, and how does it affect model performance?
I’m building two models (one for a regression problem and the other for a classification task) but I am facing low correlation in the data (lower in the classification problem than in the regression ...
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4
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Help on data transformation
I have reaction time as a dependent variable and age as an independent variable. I want to do a linear mixed model analysis. My data is not normally distributed. Should I have to transform data? I ...
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Minimize $\sum_i||Y_i-AX_i||^2$
I have N data vectors $X_i$ and N target vectors $Y_i$ where $i$ indexes the sample.
I would like to learn a linear map $A$ between the data and the target i.e find the matrix $A$ that minimize
$$\...
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Can Linear Models infer Product Sum operation of Features to predict Target?
In a dataset of 9 columns: $X_1-X_8, y$.
$y = X_1 * X_5 + X_2 * X_6 + X_3 * X_7 + X_4 * X_8$
Can any form of linear model (anything but SVM, NN, Random Forest, XGBoost, etc.) predict $y$?
- 301
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Effect on regression coefficients by multiplying a constant to a feature
I was solving one quiz question on Coursera and I found an interesting question.
If you double the value of a given feature (i.e. a specific column of
the feature matrix), what happens to the least-...
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Linear Regression and Logistic Regression
I'm a beginner, and I'm wondering whether a logistic regression in a nut-shell is just normalizing a linear regression? Correct me if I'm wrong, but I came to this conclusion because the predicted ...
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Linear Model With Highly Correlated Attributes Producing Inconsistent Weights
I know that having correlated attributes violates the linear model assumption of independent attributes, and I'm not interested in creating a more sophisticated model to tease apart the dependent ...
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What are different ways to determine how an explanatory variable affect a target variable?
I'm trying to determine a quantitative value by which a target variable change (inflation) by changing an indicator variable (interest rate).
The industry basically uses linear models such as VAR. Are ...
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What are the advantages of model drift vs concept drift in online learning?
I have asked this question here but I'm also posting it here to get a better insight:
https://stats.stackexchange.com/questions/602282/what-are-the-advantages-of-model-drift-vs-concept-drift-in-online-...
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Is Linear kernel SVM always better than Logistic regression?
We know that linear kernel SVM may give better results than logistic regression since maximizing the margin usually leads to more stable results/better displacement of the decision boundary. But is ...
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How do the intercept and slope calculated in linear regression relate to the output of lm?
I have been looking at how to calculate coefficients by hand
and the example produces
$Y = 1,383.471380 + 10.62219546 * X$
However the output shown of lm does not show these values anywhere.
How do I ...
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Linear Regression: Won't adding irrelevant features still improve the prediction
Assume we are predicting weight based on height: this is simple linear regression. If we now add gender, this creates multiple linear regression and improves our model, and makes it more capable of ...
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Is it a good idea to test the robustness of a Neural Network on a linear relation?
Just to give you more context, I'm currently working on a finance project relying on neural network. I'm principally using Neural Network to achieve regression task. So my neural network aims to ...
