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태그 feature-engineering - 이것은 페이지 3 페이지입니다 - GeneraCodice
Encoding categorical data with pre-determined dictionary
https://www.generacodice.com/ko/articolo/2688076/encoding-categorical-data-with-pre-determined-dictionary
python
-
encoding
-
scikit-learn
-
feature-engineering
-
categorical-encoding
datascience.stackexchange
Do I need to square a column if I want a neural network to try using that?
https://www.generacodice.com/ko/articolo/2686013/do-i-need-to-square-a-column-if-i-want-a-neural-network-to-try-using-that
neural-network
-
feature-engineering
datascience.stackexchange
I do feature engineering on the full dataset, is this wrong?
https://www.generacodice.com/ko/articolo/2685411/i-do-feature-engineering-on-the-full-dataset-is-this-wrong
regression
-
feature-engineering
datascience.stackexchange
KNN Regression: Distance function and/or vector representation for datetime features
https://www.generacodice.com/ko/articolo/2683236/knn-regression-distance-function-and-or-vector-representation-for-datetime-features
regression
-
distance
-
feature-engineering
-
k-nn
datascience.stackexchange
How to use id's in binary classification problem
https://www.generacodice.com/ko/articolo/2681977/how-to-use-id-s-in-binary-classification-problem
machine-learning
-
time-series
-
classification
-
supervised-learning
-
feature-engineering
datascience.stackexchange
How to use unigram and bigram as an feature on SVM or logistic regression [closed]
https://www.generacodice.com/ko/articolo/2680308/how-to-use-unigram-and-bigram-as-an-feature-on-svm-or-logistic-regression-closed
nlp
-
machine-learning
-
svm
-
logistic-regression
-
feature-engineering
datascience.stackexchange
Should I use keras or sklearn for PCA?
https://www.generacodice.com/ko/articolo/2678540/should-i-use-keras-or-sklearn-for-pca
pca
-
deep-learning
-
scikit-learn
-
keras
-
feature-engineering
datascience.stackexchange
Is there any problem with dropping only part of the OneHot generated features?
https://www.generacodice.com/ko/articolo/2678272/is-there-any-problem-with-dropping-only-part-of-the-onehot-generated-features
feature-extraction
-
feature-selection
-
feature-engineering
-
one-hot-encoding
datascience.stackexchange
How to work with Log-transformation?
https://www.generacodice.com/ko/articolo/2676868/how-to-work-with-log-transformation
regression
-
data-analysis
-
feature-scaling
-
feature-engineering
datascience.stackexchange
Handling a Pandas Data Frame containing multiple non-ordinal categorical features
https://www.generacodice.com/ko/articolo/2676272/handling-a-pandas-data-frame-containing-multiple-non-ordinal-categorical-features
python
-
machine-learning
-
feature-selection
-
feature-engineering
datascience.stackexchange
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결과가 발견되었습니다: 419