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The standard way of analyzing such data is to set up a $2\times 2$ contingency table and to run either Pearson's $\chi^2$ test or Fisher's exact test on it. Your favorite statistics software will have tools for this. For instance, in R you can use table() and chisq.test().


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A better option is to calculate Fleiss’s kappa (or Cohen's kappa if there are only two examiners) using the categorical response data of what each examiner guesses is the correct tumor type. https://www.datanovia.com/en/lessons/fleiss-kappa-in-r-for-multiple-categorical-variables/


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In effect you are asking why the chart below involves a correlation of about 0.83 rather than 1 The answer is that the points do not lie on a straight line, though high values of $x$ are still associated with high values of $y$


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If you are going to use a numerical representation using 0 or 1 you do not need to factor the categorical variable. When the value of the categorical variable is 0 then you have created your reference group (because 0 x beta = 0) so the beta of the categorical variable when x= 1 will be interpreted as the effect of having a condition present compared to the ...


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A model with "fixed effects" has individual intercepts, say $\alpha_{i}$, for each individual $i$ in your sample. This means the number of parameters you are trying to estimate grows just as quickly as your sample size $n$ does. This is called the incidental parameters problem and generally causes inconsistency of the maximum likelihood estimator. ...


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Your second model, m2 <- glmer(Sleep Status.T2 ~ Sex + Sleep Status.T1*Condition + (1|SchoolID), data=df, family = binomial) is the model I would run with your data. You want to know whether being in Condition B vs. A is associated with an increase or decrease in sleep at t2, adjusting for sleep at t1, and any covariates. The interaction further tests ...


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It makes more sense to count your 0/1 in each of the categories, for example: import pandas as pd import seaborn as sns df = pd.DataFrame({'car':['ford','tesla','bmw','tesla','ford','ford','bmw','tesla','bmw','tesla','ford','bmw'], 'TARGET_happiness':[0,1,0,1,1,1,0,1,0,0,0,1]}) sns.catplot(x='car',hue='TARGET_happiness',data=df,kind="...


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Let's assume your confusion matrix looks like this TN FP FN TP TN+FP will be number in support for negative class in sklearn. FN+TP will be number in support for positive class in sklearn. The support is the number of samples of the true response that lie in that class. Accuracy is calculated by (TN+TP)/(TN+FN+FP+TP). For weighted Recall, it's calculated ...


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I agree that your approach of bounding the entropy would work. See the this page on Information Gain However, I don't agree that this is a good method of finding the "relationship between X and Y". For that question, I recommend a logistic regression. You can also get an idea of the information gained in the regression by looking at AIC. It is ...


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