This community-built FAQ covers the “Training Set vs. Test Set” exercise from the lesson “Multiple Linear Regression”.
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This exercise can be found in the following Codecademy content:
Machine Learning
FAQs on the exercise Training Set vs. Test Set
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In the context of the Streeteasy project, the lesson shows me the following:
streeteasy = pd.read_csv(“https://raw.githubusercontent.com/sonnynomnom/Codecademy-Machine-Learning-Fundamentals/master/StreetEasy/manhattan.csv”)
df = pd.DataFrame(streeteasy)
Why is there a need to create another dataframe (df) when streeteasy is already a dataframe?
2 Likes
I understand the systematic of the proccess for obtaining the x, y train and test data but in the context of the excersice and the data we´have processed ; what is the interpretation of this arrays ? What is the conclusion given the results obtained?
There’s conflicting information in the exercises and the article on Training Set, Validation Set and Test Set.
https://www.codecademy.com/paths/machine-learning/tracks/regression-skill-path/modules/multiple-linear-regression-skill-path/lessons/multiple-linear-regression-streeteasy/exercises/training-vs-test
it says
“In general, putting 80% of your data in the training set and 20% of your data in the test set is a good place to start.”
“In general, putting 80% of your data in the training set, and 20% of your data in the validation set is a good place to start.”
Would that be for the exercise? Isn’t the test set actually an untouched data which you actually want to evaluate?
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To select all those columns… You can simply just drop the cols you don’t want
x = df.drop([‘rent’],axis=1)
y = df.rent
4 Likes
Hi! can anyone explain to me what setting a random_state integer is doing in the train_test_split? Thank you!
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According to the documentation, train_test_split seems to shuffle the datasets before splitting them by default. If we don’t specify the random_state, shuffling is random. If we specify random_state, the same shuffling will be reproduced so that the splitting is done in the same way across multiple function calls.
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Thank you! I’ve seen that written on the ww3 school too but had some trouble understanding the phrase haha. So does that mean given the same data and same random_state integer, the training sets across different runs will include identical data? Can I think of the random_state integers as a notation of a way to split?
Yes, if you set the same data and the same random_state integer, the same datasets will be output across different runs.
Probably this function shuffles the given dataset and returns a certain percentage (1 - test_size) from the beginning of the shuffled dataset as training dataset, and the rest as test dataset. If we set the random_state to a fixed integer, the result of the shuffling will be fixed, so we will get the same dataset.
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hello…why we use df[]?? is it similar with reshape(-1,1).Thanks you for you reply!
I’m a little confused about something: Why did we not use Validation Set here? And, when it is appropriate to use the Validation Set? Do we make a choice between Validation Set and Test Set?
I’m confused on the difference between df[“rent”] and df[[“rent”]] aren’t they both data frames with only the “rent” column why do I need the second set of [ ] to get the correct answer?
