Understand the idea
Logistic regression is a classifier despite its name. It models class log-odds with a linear score and uses regularisation. Scaling helps optimisation and makes regularisation more comparable across inputs.
What the syntax does
LogisticRegression- Creates a classification estimator despite the word regression in its name.
max_iter=2000- Sets an optimisation iteration limit; it does not guarantee convergence.
max_iter:100,500,1000
Supplied for this practice
These objects are prepared afresh before your editor runs.
X- Feature dataframe for the declared population, preserving row indices.
X = df[['length', 'width']] y- Target series, aligned with
X.y = df.label X_train/X_test/y_train/y_test- Training feature rows from the declared split. Final-test feature rows from the declared split. Training targets, aligned with
X_train. Final-test targets, aligned withX_test.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
Outputs you create
answer- The requested result, with the values, labels and row order described by this task.
length | width | label |
|---|---|---|
| 236.033 | -0.805568 | A |
| 581.297 | 0.728558 | A |
| -1511.27 | -1.00866 | A |
| 99.0248 | -0.24496 | A |
| -13.0141 | -0.660765 | A |
| 681.179 | 0.602475 | A |
| 51.1472 | 0.873157 | A |
| 362.131 | -0.665605 | A |
Your task
- Fit a scaled logistic classifier and predict test labels.
answerHints
Think: The classifier’s optimisation uses the prepared feature scale learned from training rows.
Tools: Pipeline, StandardScaler and LogisticRegression.
Approach: Build the named scaling/classification steps, fit the training pair and predict through the complete pipeline.
Explained solution
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
model=Pipeline([('scale',StandardScaler()),('model',LogisticRegression(max_iter=2000,random_state=42))]).fit(X_train,y_train)
answer=model.predict(X_test)
The pipeline reuses training statistics for test rows while the iteration budget supports convergence.