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You can run the shiny application, open it in chrome - using google chrome inspector you find the css class titles and add custom css.


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Load some data: # Load data library(ISLR) cars = head(ISLR::Auto,100) Split data into two parts (train and "rest"): # 70% Sample size smp_size <- floor(0.7 * nrow(cars)) # Set seed to control randomness set.seed(123) ind <- sample(seq_len(nrow(cars)), size = smp_size) # Split data train <- cars[ind, ] rest <- cars[-ind, ] Split "...


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Is the code correct? Yes, it looks correct to me How to calculate Errors: Calculate accuracy for train data Calculate accuracy for test data If train error is high (RMSE in your case) - high bias, retrain model with more trees, less learning rate, more data (if possible) If train error low, but test error high - high variance or overfit: regularization, ...


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Create addition variable: Eg: lead_time-start_time can be time to book. Reduce variables with many classes if present (part of EDA) standardize numeric variables - (val-mean)/sigma Tree is a very weak classifier, you will have to do bagging or boosting (like ada boost or gbm or random forest try parameter tuning - I am not pasting any links since I don;t ...


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This question is similar to this one and this one, but seems to be ill-posed. Either because it implies an unknown (undefined) way how the neurons process the inputs, or because the provided solution (5, 3) is wrong. Concretely, usual neurons only sum their inputs and the bias and pass the sum through a step function. These are the typical neurons used in ...


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Generally I would suggest to look for differences with the general pattern, the general pattern being the answers from most users. A very basic way to do that: Calculate for every question the proportion of text 1, store it as a vector. This distribution is the "mean vector". For every player represent the vector of their answers: for every ...


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I suggest you take a look at the TidyTuesday repo, where every week they post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. The repo also contains other resources, like data science books. Together with the repo, I suggest the TidyTuesday videos by David Robinson, where he creates screencasts of complete data ...


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Use mutate in combination with row_number as follows: df %>% mutate(row = row_number())


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It might potentially be in the compilation of your model; it has to be 'binary-crossentropy' and you have 'binary-crossentrophy' right now.


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I think if you save the plot as SVG (Scalable Vector Graphics) you can scale the image to any size. See this answer for an example. Second, if you want to print larger, you might need to tweak the DPI (Dots per inch), as done here.


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It seems it is even easier The MLE is defined as $$ \theta_{MLE} = \arg\max -(100 \ln \pi + \sum_{i=1}^{100} \ln(1 + (x_{i} - \theta)^{2})) $$ so you need to minimize the sum of logs and applying the exponential to each element of the sum does not change the result of the argmin because it is a monotone increasing function, so at the end of the day you have ...


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As far as I'm aware there is no correct/standard way to apply topic modelling, most decisions depend on the specifics of the case. So below I just give my opinion about these points: I have removed, before cleaning the data (removing mentions, stopwords, weird characters, numbers etc), all duplicate instances (having all three columns in common), in order ...


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