Encoding Categorical Data
Turning categories into numbers without inventing an order — how much label encoding really costs, why it depends on the model, what cardinality does to memory, and the leak hiding inside target encoding.
6 docs tagged with "Data Preprocessing"
Getting real data ready for a model — missing values, encoding, scaling, outliers, and the train/test split.
View all tagsFeature Scaling and Transformation
Which models need scaling and which are provably indifferent, why one outlier ruins MinMaxScaler, what log transforms actually fix, and how large the scaling leak really is.
Loading and Preparing Data
Why read_csv is not a neutral loader, the missingness it silently fails to catch, a pre-flight audit that catches it, and whether to convert to NumPy at all.
Missing Values
Why a value is missing decides what you may do about it — MCAR, MAR and MNAR, what mean imputation costs a distribution, when a smarter imputer pays, and the fit/transform contract that keeps it honest.
Outliers and Feature Engineering
What the IQR rule really flags, why outliers hide each other, the extreme point no single column can see, which treatment actually works, and the features a model cannot invent for itself.
Train / Test Split
Why a held-out set is mandatory, how much the score depends purely on random_state, and the four situations where a random split reports a number that is simply false.