learning objs cls1

Author: trevorcampbell

source/classification1.md

@@ -48,9 +48,8 @@ By the end of the chapter, readers will be able to do the following:
 - Compute, by hand, the straight-line (Euclidean) distance between points on a graph when there are two predictor variables.
 - Explain the $K$-nearest neighbor classification algorithm.
 - Perform $K$-nearest neighbor classification in Python using `scikit-learn`.
-- Use `StandardScaler` and `make_column_transformer` to preprocess data to be centered and scaled.
-- Use `sample` to preprocess data to be balanced.
-- Combine preprocessing and model training using `make_pipeline`.
+- Use methods from `scikit-learn` to center, scale, balance, and impute data as a preprocessing step.
+- Combine preprocessing and model training into a `Pipeline` using `make_pipeline`.
 
 +++