New answers tagged

1

You can try the below code to merge two file: import pandas as pd df1 = pd.read_csv(‘first.csv’) df2 = pd.read_csv(‘second.csv’) df = df1.merge(df2, on=‘Column1’)


0

There are over 300 social networks captured in the Index of Complex Networks. Thats usually the best place to start. While your quantitative method is not very clear, it sounds like you could start with networkx and use something like find_cliques. There is also a community detection method built for all types of subgraphs, not just cliques Benson et al. (...


0

By default scikit-learn's KNNImputer uses Euclidean distance metric for searching neighbors and mean for imputing values. If you have a combination of continuous and nominal variables, you should pass in a different distance metric. If you want to use another imputation function than mean, you'll have to implement that yourself.


0

To me it depends, because I would separate some types of categorical variables : Categorical variables with few classes : OneHot as fast as you can Categorical variable with some highly-represented classes and some low-represented classes : You can pre-process and regroup both low-represented classes in a huge "Other" class, and then OneHot and ...


1

You can't. If the model is trained with 6 features, it means that this model is like a function which requires 6 arguments. For instance the model might calculate the answer like this: answer = 0 * f1 + 1 * f2 + 0 * f3 + 5*f4 + 0.5*f5 +10*f6 Obviously there's no way to know the answer of this function without knowing all its arguments. Another way to look ...


0

Thanks for your answer. Actually I found out with some extensive research. The key is to define your (individual) meaning of "what you consider to be a cluster" and then derive metrics you want to benchmark those clusters with (could be silhouette coefficient, within cluster sum of squares etc.). Same goes with the assumptions you mentioned. This ...


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 ...


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 ...


2

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 ...


Top 50 recent answers are included