When working with scaled data, dummy variables should also be scaled or should be left apart without scaling? Can ML algorithms produce different result and whi
thank you in advance for your time! I'm having some trouble with the SMOTE_NC function in R (https://rdrr.io/github/dongyuanwu/RSBID/man/SMOTE_NC.html). Shortly
This is my data. I created a model with CatBoostClassifier(). I can get the feature names list with: >>> model.feature_names_ ['title', 'value'] Firs
I have a dataframe with 49 columns. Most of them are categorical (dtype object), some are numerical. As I'm a newbie in data science I tried to plot the Pearson
Never had this problem before but now it's constantly there for any piece of code I write. > sessionInfo() R version 4.0.2 (2020-06-22) Platform: x86_64-w64-
from sklearn.compose import make_column_transformer from sklearn.preprocessing import StandardScaler from feature_engine.encoding import RareLabelEncoder from f
I have a series like: df['ID'] = ['ABC123', 'IDF345', ...] I'm using scikit's LabelEncoder to convert it to numerical values to be fed into the RandomForestC