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1 vote
1 answer
85 views

I dont understand this way of having a stable train/test split even after updating the dataset

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samsamradas's user avatar
0 votes
0 answers
13 views

ML development on data biased by historical treatments

I have a dataset where in each data point was subject to certain treatment (4 different treatments) in the past based on their riskiness. The riskiness was estimated by a logistic regression model in ...
NerdyMandy's user avatar
0 votes
1 answer
40 views

Is it a problem to use the test dataset for the hyperparameter tuning, when I want to compare 2 classification algorithms on the 10 different dataset?

I know that we should use the validation set to perform hyperparameter tuning and that test dataset is not anymore really the test if it is used for hyperparameter tuning. But is this a problem if i ...
John B's user avatar
  • 1
0 votes
2 answers
68 views

Why shouldn't we try to balance the test set?

Most advice I have found online is that we must not balance the test set. The test set should remain to be unseen. However, I failed to see how balancing the test set will cause us to leak knowledge ...
Fraïssé's user avatar
  • 119
0 votes
1 answer
412 views

Imputation in train or test data

I'm having a rather simple question. Let's say i want to do a median imputation. I've read in some places that you should do: ...
Guilherme Raibolt's user avatar
0 votes
1 answer
96 views

Fairness metrics in the test set when wrong distribution

I have a doubt that we have been discussing for weeks with my colleagues and I wanted your opinion. I have a model for diagnosis of a disease and I want to know if it is fair. I train the model with ...
Esmeralda Ruiz Pujadas's user avatar
0 votes
1 answer
43 views

The meaning of P and degree of freedom in T-Test

I read about T-Test and how we can use it to compare between 2 models (https://towardsdatascience.com/paired-t-test-to-evaluate-machine-learning-classifiers-1f395a6c93fa) There are some issues I'm not ...
user3668129's user avatar
1 vote
1 answer
62 views

Test score higher than train score

I implemented a Gaussian Naive Bayes classifier and I got a test score (99,99%) higher than the train score (96,87%) Is this normal or does it mean that my model is underfitting ? Thank you.
biihu's user avatar
  • 11
0 votes
1 answer
42 views

How to demonstrate two variables are orthogonal with respect to the output in a 3-D Python dataset?

I have a Python dataset with 300 samples and 3 columns: 2 independent integer variables X,Y and the dependent continuous variable ...
Adrián Pérez Diéguez's user avatar
0 votes
2 answers
1k views

Why label encoding before split is data leakage?

I want to ask why Label Encoding before train test split is considered data leakage? From my point of view, it is not. Because, for example, you encode "good" to 2, "neutral" to 1 ...
Anar's user avatar
  • 73
0 votes
1 answer
28 views

How to address label imbalance in deciding train/test splits?

I'm working on a dataset that isn't split into test and train set by default and I'm a bit concerned about the imbalance between the 'label' distributions between them and how they might affect the ...
civy's user avatar
  • 101
0 votes
1 answer
151 views

Is it good to use .fit to xtest when we use PolynomialFeatures() of sklearn?

My teacher did this in class, and I'm wondering is this ok to use .fit_transform with xtest? It shouldn't just be poly.transform(xtest) Teacher's Code ...
JEAN LEONARDO 's user avatar
1 vote
2 answers
136 views

Updating a train/val/test set

It is considered best practice to split your data into a train and test set at the start of a data science / machine learnign project (and then your train set further into a validation set for ...
Aesir's user avatar
  • 458
2 votes
2 answers
360 views

Dataset and why use evaluate()?

I am starting in Machine Learning, and I have doubts about some concepts. I've read we need to split our dataset into training, validation and test sets. I'll ask four questions related to them. 1 - ...
Murilo's user avatar
  • 125