Feature Engineering and Descriptive Statistics

One-hot encoding

Converts categorical values into binary vectors/columns.

Binning

Groups numeric values into categories, such as small/medium/large or age ranges.

Combining features

Merging features to create a simpler or more useful feature.

Dropping features

Removing features that do not help the model.

Derived features

New features created from existing data.

Interaction feature

Feature created by combining two or more inputs to capture their joint effect.

Recursive feature elimination

Feature selection method that repeatedly removes less useful features.

Descriptive statistics

Summary measures such as mean, median, range, variance, and standard deviation.

Central tendency

Typical/center value of a dataset; mean and median are common measures.

Mean

Sum of values divided by number of values; central tendency measure.

Median

Middle value after sorting; another central tendency measure.

Range

Maximum minus minimum; measures spread.

Variance

Average squared distance from the mean; measures spread.

Standard deviation

Typical distance from the mean; square root of variance.