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Practically everything related to statistics (including Machine Learning) has to do with studying chance, i.e. trying to determine to what extent an observation is due to chance or not. For example one might want to know whether a drug actually helps with a particular disease or not. If we observe that one patient improves after taking the drug, there's ...


3

They are not categorical as they have a meaningful ordering that you likely want to use. The first is usable as is as it is roughly fraction religious times 10. Yes it's ordinal but happens to be just a rescaled continuous feature. The second is ordinal and so you don't quite want to use it as a continuous feature as that doesn't capture the difference in ...


3

In addition to Erwan's answer, which gives great general advice, consider the following questions when you are deciding rather to keep data. What question(s) are you trying to answer? What are you trying to learn from the data? If you are trying to build a model that will predict patient recovery based on drug administered and a variety of other ...


2

My answer would be second option I think the use of PCA is to represent original high dimensional information/data in lower dimension by calculationg the direction/axes along which there is maximum variablity in data. In first case, where you filter for 0-labeled observations and then do PCA so PCA would measure variablity based on a smaller version of ...


1

Ideally, the threshold should be selected on your training set. Your holdout set is just there to double confirm that whatever has worked on your training set will generalize to images outside of the training set. This is the reason why hyperparameters tuning like GridSearch and RandomizedSearch in python has a cv parameter to cross-validate between ...


1

Clearly there's no way to have the names of the drugs. Assuming the relation between the two columns is important, a scatter plot with units prescribed as X and number of patients as Y might work. You could even add the name of the drug for a few isolated points. Transparency/opacity can be used to show the dense areas. In case the relation between the ...


1

You always choose the statement that you want to disprove as Null hypothesis. You can either reject it in favour of alternative hypothesis or not reject it. We don't say we accept null hypothesis. Because we don't have enough evidence for the null hypothesis to be true. It might be true, might be not. But with significant t/p value we can reject null ...


1

So the problem is how to visualise your box plots so that they appear in the same plot (axes). To do this, it is simply a minor alteration to your code. f, axes = plt.subplots(1, 2) sns.boxplot(x="status",y="assets" ,data=df1, palette="Set3",ax=axes[0]) sns.boxplot(x="status",y="assets" ,data=df2, palette=&...


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One option is to reframe it as a word embedding problem. Emojis can be embedded in a vector space along with comments and hashtags. Then distance measures and clustering can be used to find the emojis that are associated with different sentiments.


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