Pipeline with GridSearchCV in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pipeline with a scaler and a logistic regression model.

ML Python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([('scaler', StandardScaler()), ('clf', [1])])

Drag options to blanks, or click blank then click option'

AKNeighborsClassifier()

BRandomForestClassifier()

CLogisticRegression()

DSVC()

Attempts:

3 left

💡 Hint

Common Mistakes

Using a classifier other than LogisticRegression when the task specifies logistic regression.

Forgetting to instantiate the model with parentheses.

The pipeline requires a logistic regression model instance as the classifier step.

2fill in blank

medium

Complete the code to define the parameter grid for GridSearchCV to tune logistic regression's C parameter.

ML Python

param_grid = {'clf__[1]': [0.1, 1, 10]}

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Asolver

Bpenalty

Cmax_iter

DC

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'penalty' instead of 'C' for regularization strength.

Not prefixing the parameter with 'clf__' to target the pipeline step.

The 'C' parameter controls the inverse of regularization strength in logistic regression.

3fill in blank

hard

Fix the error in the GridSearchCV instantiation by completing the missing argument.

ML Python

from sklearn.model_selection import GridSearchCV

grid_search = GridSearchCV(pipeline, param_grid, [1]=5)

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Acv

Bscoring

Cverbose

Dn_jobs

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scoring' instead of 'cv' for cross-validation folds.

Omitting the 'cv' argument leading to default behavior.

The 'cv' argument sets the number of cross-validation folds in GridSearchCV.

4fill in blank

hard

Fill both blanks to fit the grid search on training data and get the best parameters.

ML Python

grid_search.[1](X_train, y_train)
best_params = grid_search.[2]

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Afit

Bpredict

Cbest_params_

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' instead of 'fit' to train the model.

Trying to access 'best_params' without the trailing underscore.

You fit the grid search with training data and then access best_params_ to get the best parameters.

5fill in blank

hard

Fill all three blanks to predict on test data, calculate accuracy, and print the result.

ML Python

from sklearn.metrics import [1]
predictions = grid_search.[2](X_test)
accuracy = [3](y_test, predictions)
print(f'Accuracy: {accuracy:.2f}')

Drag options to blanks, or click blank then click option'

Aaccuracy_score

Bpredict

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using confusion_matrix instead of accuracy_score for metric.

Calling predict on pipeline instead of grid_search.

Import accuracy_score, use grid_search.predict to get predictions, then calculate accuracy_score with true labels.

Practice

1. What is the main purpose of using a Pipeline in machine learning?

easy

A. To combine preprocessing steps and model training into one object

B. To speed up the training by using multiple CPUs

C. To automatically select the best model type

D. To visualize the model's decision boundaries

Solution

1. Step 1: Understand what a Pipeline does

   A Pipeline chains preprocessing and model training steps so they run together smoothly.

2. Step 2: Identify the main benefit

   This chaining helps avoid mistakes and makes code cleaner by combining steps into one object.

3. Final Answer:

   To combine preprocessing steps and model training into one object -> Option A

4. Quick Check:

   Pipeline = combine steps [OK]

Hint: Pipeline bundles steps to simplify workflow [OK]

Common Mistakes:

• Thinking Pipeline speeds up training automatically
• Confusing Pipeline with model selection
• Believing Pipeline creates visualizations

2. Which syntax correctly sets the parameter n_estimators of a RandomForest inside a pipeline named pipe for GridSearchCV?

easy

A. {'randomforest-n_estimators': [10, 50, 100]}

B. {'random_forest__n_estimators': [10, 50, 100]}

C. {'randomforest.n_estimators': [10, 50, 100]}

D. {'randomforest__n_estimators': [10, 50, 100]}

Solution

1. Step 1: Recall parameter naming in Pipeline

   Parameters inside a pipeline step use double underscores: stepname__paramname.

2. Step 2: Match step name and parameter

   If the step is named 'randomforest', then 'randomforest__n_estimators' is correct syntax.

3. Final Answer:

   {'randomforest__n_estimators': [10, 50, 100]} -> Option D

4. Quick Check:

   Use double underscores between step and param [OK]

Hint: Use double underscores between step and parameter [OK]

Common Mistakes:

• Using single underscore instead of double
• Using dot or dash instead of double underscore
• Misspelling the pipeline step name

3. Given the code below, what will grid.best_params_ output?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', RandomForestClassifier(random_state=42))
])

param_grid = {'clf__n_estimators': [20], 'clf__max_depth': [4]}
grid = GridSearchCV(pipe, param_grid, cv=2)
grid.fit(X_train, y_train)

print(grid.best_params_)

medium

A. SyntaxError due to param_grid keys

B. {'clf__n_estimators': 10, 'clf__max_depth': 2}

C. {'clf__n_estimators': 20, 'clf__max_depth': 4}

D. KeyError because 'clf' is not a pipeline step

Solution

1. Step 1: Understand pipeline and param_grid

   The pipeline has a step named 'clf' for RandomForestClassifier. The param_grid uses 'clf__' prefix correctly.

2. Step 2: Determine the output

   Since param_grid specifies only one combination, GridSearchCV will select {'clf__n_estimators': 20, 'clf__max_depth': 4} as the best parameters.

3. Final Answer:

   {'clf__n_estimators': 20, 'clf__max_depth': 4} -> Option C

4. Quick Check:

   Best params match the only tested values [OK]

Hint: With single param values, they become best_params_ [OK]

Common Mistakes:

• Confusing step name 'clf' with 'classifier'
• Using single underscore in param_grid keys
• Assuming syntax error without checking keys

4. Identify the error in this pipeline and GridSearchCV setup:

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('model', RandomForestClassifier())
])

param_grid = {'randomforest__n_estimators': [10, 50]}
grid = GridSearchCV(pipe, param_grid)
grid.fit(X_train, y_train)

medium

A. The param_grid key should be 'model__n_estimators', not 'randomforest__n_estimators'

B. RandomForestClassifier cannot be used inside a pipeline

C. StandardScaler should not be the first step

D. GridSearchCV requires cv parameter

Solution

1. Step 1: Check pipeline step names

   The pipeline step for RandomForestClassifier is named 'model', not 'randomforest'.

2. Step 2: Match param_grid keys to pipeline steps

   Parameter keys must use the step name 'model' with double underscores, so 'model__n_estimators' is correct.

3. Final Answer:

   The param_grid key should be 'model__n_estimators', not 'randomforest__n_estimators' -> Option A

4. Quick Check:

   Param keys must match pipeline step names [OK]

Hint: Param keys must match pipeline step names exactly [OK]

Common Mistakes:

• Using wrong step name in param_grid keys
• Thinking RandomForest can't be in pipeline
• Believing cv is mandatory (it defaults to 5)

5. You want to tune both a scaler and a classifier in a pipeline using GridSearchCV. Which param_grid correctly tests StandardScaler with and without scaling, and RandomForest with 10 or 50 trees?

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', RandomForestClassifier(random_state=0))
])

param_grid = ?

hard

A. {'scaler__': [StandardScaler(), None], 'clf__n_estimators': [10, 50]}

B. {'scaler': [StandardScaler(), None], 'clf__n_estimators': [10, 50]}

C. {'scaler': [StandardScaler(), None], 'clf__n_estimators': [10, 50], 'clf__max_depth': [None]}

D. {'scaler__with_mean': [True, False], 'clf__n_estimators': [10, 50]}

Solution

1. Step 1: Understand how to toggle scaler on/off in pipeline

   To test with and without scaling, replace the scaler step with StandardScaler() or None in param_grid using the step name 'scaler'.

2. Step 2: Set classifier parameters correctly

   Use 'clf__n_estimators' to test 10 and 50 trees for the RandomForestClassifier step named 'clf'.

3. Final Answer:

   {'scaler': [StandardScaler(), None], 'clf__n_estimators': [10, 50]} -> Option B

4. Quick Check:

   Toggle scaler by replacing step, tune clf params with double underscores [OK]

Hint: Toggle steps by replacing with None in param_grid [OK]

Common Mistakes:

• Trying to set scaler params with double underscores incorrectly
• Using 'scaler__' key with no param name
• Not using None to disable a step