evaluation

问题 Can someone explain why does the random_state parameter affects the model so much? I have a RandomForestClassifier model and want to set the random_state (for reproducibility pourpouses), but depending on the value I use I get very different values on my overall evaluation metric (F1 score) For example, I tried to fit the same model with 100 different random_state values and after the training ad testing the smallest F1 was 0.64516129 and the largest 0.808823529). That is a huge difference.

问题 Related question - 1 I have a dataset like so: > head(training_data) year month channelGrouping visitStartTime visitNumber timeSinceLastVisit browser 1 2016 October Social 1477775021 1 0 Chrome 2 2016 September Social 1473037945 1 0 Safari 3 2017 July Organic Search 1500305542 1 0 Chrome 4 2017 July Organic Search 1500322111 2 16569 Chrome 5 2016 August Social 1471890172 1 0 Safari 6 2017 May Direct 1495146428 1 0 Chrome operatingSystem isMobile continent subContinent country source medium 1

问题 Related question - 1 I have a dataset like so: > head(training_data) year month channelGrouping visitStartTime visitNumber timeSinceLastVisit browser 1 2016 October Social 1477775021 1 0 Chrome 2 2016 September Social 1473037945 1 0 Safari 3 2017 July Organic Search 1500305542 1 0 Chrome 4 2017 July Organic Search 1500322111 2 16569 Chrome 5 2016 August Social 1471890172 1 0 Safari 6 2017 May Direct 1495146428 1 0 Chrome operatingSystem isMobile continent subContinent country source medium 1

问题 I realized I've been switching between them with no understanding as to why, and am finding it hard to search for. 回答1: ' ' and " " are the same thing; they are used to define string literals. Things without quotes can be an identifier, keyword, non-string literal, property name or a number (may have missed one). Examples: "hello world" literal (string) 'hello world' literal (string) with same contents document identifier (object) { a: 1 } property name if keyword (start conditional statement

问题 I realized I've been switching between them with no understanding as to why, and am finding it hard to search for. 回答1: ' ' and " " are the same thing; they are used to define string literals. Things without quotes can be an identifier, keyword, non-string literal, property name or a number (may have missed one). Examples: "hello world" literal (string) 'hello world' literal (string) with same contents document identifier (object) { a: 1 } property name if keyword (start conditional statement

问题 I realized I've been switching between them with no understanding as to why, and am finding it hard to search for. 回答1: ' ' and " " are the same thing; they are used to define string literals. Things without quotes can be an identifier, keyword, non-string literal, property name or a number (may have missed one). Examples: "hello world" literal (string) 'hello world' literal (string) with same contents document identifier (object) { a: 1 } property name if keyword (start conditional statement

问题 Say we have a data.table myDT <- data.table(id = c("a", "a", "b", "b", "c"), value = 1:5) setkey(myDT, id) I'd like to create a function fun <- function(id) { ... } such that if foo <- rep("b", 6) then fun(foo) # I want this to return 3 4 Basically, I want to pass id[[1]] from the execution environment to the i argument of myDT . I'm having a really hard time accessing the correct environment here and am looking for some help. Changing the name of the function argument is not an option. 回答1:

问题 I've got several large-ish data.frames set up like a relational database, and I'd like to make a single function to look for whatever variable I need and grab it from that particular data.frame and add it to the data.frame I'm currently working on. I've got a way to do this that works, but it requires temporarily making a list of all the data.frames, which seems inefficient. I suspect that nonstandard evaluation would solve this problem for me, but I'm not sure how to do it. Here's what works

问题 I am trying to compute the micro F measure for a prediction my model did. I trained the model using word2vec Vectors with Keras and Tensorflow. I use the scikit library to compute the mirco F measure. But the function throws this message: ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets Also, am i doing the prediction right? I trained the model on x_train(wordVectors) and y_train(resultVectors) and validated with x_test and y

问题 int a = 1, b = 2; int c = a*b + b==0; // c = 0 cout << a*b + b==0; // outputs 4 c evaluates to 0 because the operator precedence of the * and + operators is higher than == as a result of which c essentially evaluates to (a*b+b)==0 which is false. Why does putting the same expression in a cout statement output 4? 回答1: Because the precedence of these operators are operator* > operator+ > operator<< > operator== . Then cout << a*b + b==0; is equivalent with (cout << ((a*b) + b)) == 0; . Then the