DATA SCIENCE PYTHON PLAYGROUND

A lower training error is not enough.

PolynomialFeatures, StandardScaler, Ridge, Pipeline and GridSearchCV.

Revisit: Regression checkpoint

Compare both candidates on the same folds before choosing and opening final evidence.

This complete script replaces the editor contents. It includes the input setup and the workflow.

import numpy as np
import pandas as pd
df=pd.read_csv('data/ml-learning/ML-X02.csv')
from sklearn.base import clone
from sklearn.model_selection import train_test_split, KFold, cross_validate, cross_val_predict, GridSearchCV
from sklearn.dummy import DummyRegressor
from sklearn.metrics import root_mean_squared_error

import matplotlib.pyplot as plt
import seaborn as sns
X=df[['sugarpercent']]
y=df['winpercent']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state=42)
folds=KFold(n_splits=5,shuffle=True,random_state=42)
candidates={}
grids={}
# Prepare inputs and construct the candidate pipeline.
preprocessor = 'passthrough'
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
model = LinearRegression()
pipeline = Pipeline([('prepare', preprocessor), ('model', model)])
model = pipeline
candidates['simple_linear']=model
grids['simple_linear']=None
# Prepare inputs and construct the candidate pipeline.
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.linear_model import Ridge
polynomial = PolynomialFeatures(degree=2, include_bias=False)
scale = StandardScaler()
model = Ridge()
pipeline = Pipeline([('polynomial', polynomial), ('scale', scale), ('model', model)])
model = pipeline
candidates['polynomial']=model
grids['polynomial']={'polynomial__degree': [2, 3]}
# Validate initial candidates on matching training folds.
initial_results={name:cross_validate(candidate,X_train,y_train,cv=folds,scoring='neg_root_mean_squared_error') for name,candidate in candidates.items()}
# Compare with a simple reference before tuning.
reference_results=cross_validate(DummyRegressor(strategy='mean'),X_train,y_train,cv=folds,scoring='neg_root_mean_squared_error')
rows=[]
selected={}
for name,candidate in candidates.items():
    initial=initial_results[name]
    grid=grids[name]
    if grid:
        search=GridSearchCV(candidate,grid,cv=folds,scoring='neg_root_mean_squared_error',n_jobs=1)
        search.fit(X_train,y_train)
        selected[name]=search.best_estimator_
        validation_score=float(search.best_score_)
        settings=search.best_params_
    else:
        selected[name]=candidate
        validation_score=float(np.mean(initial['test_score']))
        settings='Keep defaults: no production search'
    rows.append({'model':name,'initial_score':float(np.mean(initial['test_score'])),'selected_score':validation_score,'settings':str(settings)})
cv_results=pd.DataFrame(rows).set_index('model')
# This solution nominates by mean training-fold score. Other evidence-based choices can be defensible.
chosen_name=cv_results.selected_score.idxmax()
chosen=selected[chosen_name]
oof_predictions=cross_val_predict(chosen,X_train,y_train,cv=folds)
diagnostic_y=y_train
residuals=pd.DataFrame({'actual':diagnostic_y,'predicted':oof_predictions,'residual':diagnostic_y-oof_predictions})
fig,ax=plt.subplots(figsize=(6,4))
sns.scatterplot(x=residuals.predicted,y=residuals.residual,s=12,ax=ax)
ax.axhline(0,color='black')
ax.set(xlabel='Training-validation prediction',ylabel='Residual',title='Training-only residuals')
fig.tight_layout()
fig.savefig('diagnostic.png',dpi=150,bbox_inches='tight')
final_model=clone(chosen).fit(X_train,y_train)
final_predictions=final_model.predict(X_test)
final_rmse=root_mean_squared_error(y_test,final_predictions)
print('Final RMSE:',final_rmse)

import numpy as np
import pandas as pd
df=pd.read_csv('data/ml-learning/ML-X02.csv')

Why these steps exist

• Matching folds compare the line with the complete expansion–scale–Ridge recipe. Degree selection uses training evidence, so extra flexibility must earn its place before the nominated workflow predicts final rows. Residuals and fold variation qualify a small score difference.

Another valid approach

Equivalent ordinary Python is welcome. A defensible candidate or grouping need not match the solution’s choice; objective evidence must remain consistent.