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This question is evidence that the scientific method has sort of gotten lost in the way the ML world communicates about models, and it causes students to get confused when they enter industry jobs. Let's illustrate the difference through the metaphor of a soccer team training for the upcoming season. Most of the time will be spent doing drills for core ...


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You are correct. They are not independent and they are positively correlated. Let $A$ and $B$ (or $X$ and $Y$) be two events for stating general theorems and $M$ and $T$ be the events "customer purchases mozzarella" and "customer purchases tomatoes" in this specific example. We will use $\wedge$ to mean "and" so that $M \wedge T$...


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Usually you first split your dataset into train-/test- set, and then if your model training process requires validation set, you can further split your train-set into the final train-set and the validation-set. Simple rule is test set never shows up in your model development process, including when you develop your data preprocessing steps (such as your data ...


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You can use the testing data to perform hyperparameters optimization to see which hyperparameters of your model pipeline work the best. The validation data is then only used once to see how the whole model pipeline works on out of sample data. For this process the test dataset cannot be used again as this data was already used to select the best ...


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Removing outliers in a high-dimensional scenario can for example be done after dimension reduction by principal component analysis. In the dimension-reduced space either boxplots (1 dimension), bagplots (2 dimension) or gemplots (3 dimensions) can be applied to detect outliers. For details please look at Kruppa, J., & Jung, K. (2017). Automated ...


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Given you have two categorical variables and the associated contingency table, one option is to calculate the joint and marginal probabilities: $$ P = \frac{count}{total}$$ Probabilities Tomatoes No Tomatoes Row Mozzarella 0.4 0.1 0.5 No Mozzarella 0.2 0.3 0.5 Column 0.6 0.4 0.1 The probabilities then can be used to answer questions about the data - If ...


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Market Basket Analysis is the way to go about it. Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together. Market Basket Analysis creates If-Then scenario rules, for example, if item A ...


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The article you have shared answers the question correctly - your algorithm could input both int and float, and its internals (weights, biases, etc) will all be float matrices. A common example that you will find is the "customer churn" datasets, in which you need to classify whether a customer will stay/leave a company to go to a competitor. The ...


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Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together. Market Basket Analysis creates If-Then scenario rules, for example, if item A is purchased then item B is likely to be ...


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These string data, called categorical data can be converted to numerical data using many Categorical Encoding Techniques. Encoding categorical data is a process of converting categorical data into integer format so that the data with converted categorical values can be provided to the different models. Types of Categorical Techniques: Backward Difference ...


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You can multiple things here : Converting them to numerical introduces some sense of ordering For example if you say slovenia is 1 and USA is 2 ans ordering is introduced instead you can use one hot encoding. Pandas getdummies function will do it for you If one of your string has a lot of values say 1000 one hot encoding does not makes sense. In those ...


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It seems that the function tries to call the SPLAT! command line using srtm2sdf on all files in in_path. Trying to run a command line program using subprocess.run when the command line program doesn't exist (i.e. returns a 'command not found' error when trying to run a command on the command line) gives a FileNotFoundError instead of the actual error you get ...


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The message tells you that there is no variable mcq_grade. Most likely when you ran the code yourself you had already loaded some data from a file and assigned it to mcq_grade, it could look like this: mcq_grade <- read.table(filename) All the code that you put in your R markdown source document must be independent because it's executed in a new ...


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Yes, you can train XGBoost in parallel using the Dask backend. Short Solution Training XGBoost in parallel with Dask requires 2 changes in your code: substitute dtrain = xgb.DMatrix(X_train, y_train) with dtrain = xgb.dask.DaskDMatrix(X_train, y_train) substitute xgb.train(params, dtrain, ...) with xgb.dask.train(client, params, dtrain, ...)


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The problem seems to be that you are dividing by 10 (k) at each iteration, I can think to try to calculate the average, this is incorrect and probably it is what is causing you to see a very low metric value. It would be simpler and correct, to only store the values for the metric in each iteration at the validation set and finally just calculate the average ...


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