Neural regression workflow · Data Playground

Understand the idea

The feature scaler acts on X. TransformedTargetRegressor learns a separate transformation of y during each fit and inverse-transforms predictions automatically. Evaluate returned predictions in original target units.

Schematic · Keep feature scaling and target transformation distinct.Scroll the diagram horizontally if needed.

Python skill: Fits the target transformation inside each regressor fit and reverses it during prediction.

Meet the syntax

TransformedTargetRegressor(regressor=MLPRegressor(...), transformer=StandardScaler())
model__regressor__hidden_layer_sizes

TransformedTargetRegressor: Fits the target transformation inside each regressor fit and reverses it during prediction.
regressor=MLPRegressor(...): Places neural regression inside the target wrapper.
transformer=StandardScaler(): Scales y within that wrapper; feature scaling belongs in the outer input pipeline.
model__regressor__hidden_layer_sizes: Addresses neural hidden widths through the pipeline’s target-wrapper step during search.

Follow the code

Use the numbered comments to connect each Python block to the workflow above.

model.fit(X_train,y_train)
answer=model.named_steps['model'].transformer_.mean_

This practice: Read and run the Python. Next: Change · Neural regression workflow.

Neural regression · from idea to workflow

Question: Can learned nonlinear combinations predict a quantity?

Mechanism: Hidden units transform scaled features; the target wrapper returns predictions in original units.

Watch for: Unstable optimisation and overfitting can dominate small datasets.

Interpret or debug · self-review: Check original target units and loss history, then compare a simple baseline on matching folds.

Independent full workflow: ML-X15. First practise complete regression and classification in F11–F12; check readiness in W-K2–W-K3.

Given data · Wine600

600 observations. One red or white wine sample. The dataframe df is supplied afresh for each Run.

Download source CSV · Source and original dictionary

Wine600 · first 8 prepared rows
fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	wine_type
5.3	0.32	0.12	6.6	0.043	22	141	0.9937	3.36	0.6	10.4	6	white
6.4	0.3	0.27	4.4	0.055	17	135	0.9925	3.23	0.44	12.2	6	white
6.5	0.22	0.32	2.2	0.028	36	92	0.99076	3.27	0.59	11.9	7	white
8	0.24	0.48	6.8	0.047	13	134	0.99616	3.23	0.7	10	5	white
8.9	0.29	0.35	1.9	0.067	25	57	0.997	3.18	1.36	10.3	6	red
6.6	0.21	0.39	2.3	0.041	31	102	0.99221	3.22	0.58	10.9	7	white
8.8	0.24	0.54	2.5	0.083	25	57	0.9983	3.39	0.54	9.2	5	red
5.4	0.15	0.32	2.5	0.037	10	51	0.98878	3.04	0.58	12.6	6	white

Column meanings and units

quality: ordered sensory score (0–10). alcohol: volume percent. pH: acidity scale. Other chemistry units follow the linked source dictionary.

The score is ordinal but modelled as regression here. ML removes exact duplicate rows before splitting. Chemistry must be available at prediction time; predictive associations do not establish effects of changing an ingredient.

Input schema
Column	Stored type
fixed acidity	float64
volatile acidity	float64
citric acid	float64
residual sugar	float64
chlorides	float64
free sulfur dioxide	float64
total sulfur dioxide	float64
density	float64
pH	float64
sulphates	float64
alcohol	float64
quality	int64
wine_type	str

Supplied setup · available if you need to inspect it

This code runs before your editor on every Run. These are the objects your exercise uses.

from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import TransformedTargetRegressor
from sklearn.neural_network import MLPRegressor
X=df.drop(columns=['quality','wine_type'])
y=df.quality
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state=42)
model=Pipeline([('scale',StandardScaler()),('model',TransformedTargetRegressor(regressor=MLPRegressor(hidden_layer_sizes=(24,),max_iter=800,early_stopping=True,tol=1e-3,random_state=42),transformer=StandardScaler()))])

Your task · Follow

Fit the supplied feature pipeline and target wrapper.

Hint 1 — Think

The target wrapper learns a separate transformation of y during fit.

Hint 2 — Tools

TransformedTargetRegressor and its fitted transformer_.

Hint 3 — Approach

Fit the full supplied recipe and inspect the target transformer’s learned mean.

Explained solution

model.fit(X_train,y_train)
answer=model.named_steps['model'].transformer_.mean_

The target scale is learned from training outcomes within the wrapper, rather than globally before validation.

Helpful prior knowledge: Convergence, early stopping and validation · Read residual patterns and limits These links are guidance, not locks.

Sources and API context

Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.