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You could think of histogram as one way of plotting a distribution of values. Another way of plotting such distribution is a KDE (Kernel Density Estimate), but after all, these inform about the same concept, which is how frequent is each value (or values interval) of your values series (x-axis). I like this picture from seaborn distplot to have a one shot ...


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Cochran's Q test Is a generalisation of the McNemars test and can be used to see if there is a truly better classifier for the metric chosen. You can ofcourse also do pairwise Mcnemare test and draw conclusions from there. NOTE: These things are expensive


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Good question. Lots of options. Most would recommend you use CDFs instead of histograms. So convert your observed distribution to empirical CDF (in R it’s ecdf() - I dint know python). Then, plot another line that is the theoretical CDF using your best-fit parameters over the span of a to b, where a to b gives you at least the same coverage as your ...


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You can use class ratio / sample class ratio. Which will make it more intuitive for any reader while going through the details. As its not used for model performance analysis hence I think we don’t have a metric name for this.


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First figure shows Frequency(Y-axis) distribution over varied values of Line data(X-axis). Similar information gets conveyed by your second figure as well, but second one provides a deeper insight to frequency fluctuation over smaller bins of Line data. Additionally in second figure, various types (Lognorm, Exponential, etc.) of distribution gets line traced ...


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To start with, you could use a simple thresholding. If you have the dataset $S$ where an element has the form $(x,y,c) \in S$, $x$ denotes the year, $y$ is a binary value (exam passed or not), and $c$ is the student id. you can obtain a classifier by using $\{(x,y,c) \in S \mid x \leq \theta\}$ and $\{(x,y,c) \in S \mid x > \theta\}$. Now you can check ...


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Most probably, you are using Pearson's correlation method. This method is used for two Continuous features. Here, both the price_drop and the OHE features are Binary Categorical features. So, you can use these methods - Phi - Phi is a measure of the degree of association between two binary variables (two categorical variables, each of which can have ...


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assuming your data is stored in an object named df you can do: tapply(df$S_Calls, df$Emp_Stat, median) As for the mode, oddly enough R does not have a built in function for that. You could define one yourself using: mode_stat <- function(x) { ux <- na.omit(unique(x)) ux[which.max(tabulate(match(x, ux)))] } and then do in a similar fashion: ...


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The distribution of a variable is an abstract concept which represents how the variable is "distributed", that is it represents the chances that the variable has any particular value. For example if the variable is the outcome of a regular dice, then any of the values 1 to 6 has the same chances to appear (1/6). This is a uniform distribution. If you ...


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This solved the problem: select distinct on (customer) customer, agent, min(call) from call_data; I am using PostgreSQL.


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This is not necessarily unexpected (or broken). Imagine that users tend to use none or all of the applications. For a specific example, suppose 90 users use no apps at all, and 10 use all (say) 11 of them. Then the average apps used by a user is $(90\cdot0+10\cdot11)/100=1.1$, but for each app, the average app-usage of a user who uses that app is $(10\...


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Janez Demsar has published an article concerning comparison of different classifiers. When you're using multiple datasets to check which algorithm performs best, assuming, that quality measurements come from normal distribution can be risky, so ANOVA is not necessarily recommended. (With ~8k citations it's a canonical article about comparison of classifiers.)...


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It's similar to the concept of odds. The baseline count of positive and negative samples gives you an odds (all else being equal) that a random sample from the population will be positive or negative.


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This is Imbalance Class Dataset concept. Mostly the ratio you mentioned is used as: # of positive class (minority) / # of negative class (majority) For example: "The dataset contains 100 fraud activities among 10000 transactions with 0.01 class imbalance." There are no strict rule/metric about that you can also use other version with 100 class imbalance ...


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It can be a good idea, but if you want to use PCA, you will have to use it carefully. First of all, PCA will reduce dimension depending on the data observed in your dataset. Consequently, if it is biaised somehow, the projection will not work with different datasets. For instance, if you have a strong correlation between two features and a third one is ...


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PCA will change your data and you will not be able to interpret it in sane sense, you can just slice and dice the data and do many things by hand, PCA would be usefull if you would want to find "neighbouring" players in terms of raw statistics but it can be deceptive because PCA don't know which stats are important, if you want to decrease dimensionality of ...


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I see. Yes PCA is a good tool that you can use on your choice of attributes. The tricky part is that PCA is based on variance. Hence, if you have 2 attributes which are more important and have low variance and then 5 more with a lot of variances then the latter will be predominant on the first PC (I guess some standardisation would help). Hence, maybe it ...


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