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I think you are confusing the term "dataset" with "features". "Social media reactions" and "weather" are features (commonly denoted as the "X" matrix) from your problem, since they will be used to predict the people's sentiments, which is your target variable (commonly denoted as the "y" vector). Then, "social media reactions" and "weather" together compose ...


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The basic option, as in the effects package is to plot fitted values against one of the interacting regressors while fixing the other regressor at a representative value (say, mean, median or any "interesting" value). For effects package syntax and examples, see https://cran.r-project.org/web/packages/sjPlot/vignettes/plot_interactions.html Alternatively, ...


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For biomedical studies, a general rule of thumb to avoid overfitting in an unpenalized logistic regression model is to have on the order of 10-20 minority-class cases per evaluated predictor. You have 11 cases in the minority class, so without penalization should only be evaluating 1 predictor. That predictor would need to be pre-selected based on your ...


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The optimizer state is the optimizer's momentum vector or similar history-tracking properties. For example, the Adam optimizer tracks moving averages of the gradient and squared gradient. If you start training a model without restoring these data, the optimizer will operate differently. The updates will be different, so the optimizer will proceed along a ...


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Based on those definitions: Nonparametric:Algorithms that do not make strong assumptions about the form of the mapping function are called nonparametric machine learning algorithms. By not making assumptions, they are free to learn any functional form from the training data. EX: k-Nearest Neighbors, Decision Trees Parametric:Assumptions can greatly ...


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In general, there's not necessarily any relationship between an estimator's bias and the sample size. However, estimators are often constructed so that their bias and variance decline as the sample size grows. As a trivial counterexample, consider the case where you have some data $X_1, \dots, X_n$, and you'd like to estimate their mean. Now, a valid ...


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Considering you're comfortable with the idea of making subjective assumptions, and it's not a model that will determine if someone lives or dies, I would suggest a Bayesian model might be a good option. This would allow you to state your assumptions explicitly and visualize them in a graph, so it can be audited and interpreted in a fairly straightforward ...


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The approach that is currently used is wrong for many reasons, see Algorithms for automatic model selection for a nice summary. As for alternatives, there are two (three) main ones, depending on your goal: As you suggested, use domain knowledge to filter the data and then run one model, in which case inference holds and can be reported for all variables. ...


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it depends on the signification of the parameter if for example 'a' is a parameter depends on the mean so you should find a rough estimation for the mean.


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If your variables can all be treated as continuous and the predictors are linearly related to outcome, then consider the following 2 models: $$ Y = \beta_0 + \beta_1X + \beta_2M + \beta_3XM+\epsilon,$$ and $$ Y = \beta_0 + \beta_1X + \beta_2M+\epsilon,$$ where the $\beta$ values are linear regression coefficients and $\epsilon$ represents the error ...


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What is variable M? If it is categorical, you could potentially control for by stratifying your analysis, which would then require you to use stratified Fisher's exact test if cell sizes of expected values are at or less than 5. Seeing as your sample size is 30, there will definitely be some cell sizes at or below that number. If it is not categorical, I ...


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