Pipeline with GridSearchCV in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of GridSearchCV best parameters in pipeline

What will be the output of the following code snippet after fitting the GridSearchCV pipeline?

ML Python

from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

iris = load_iris()
X, y = iris.data, iris.target

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('svc', SVC())
])

param_grid = {
    'svc__C': [0.1, 1],
    'svc__kernel': ['linear', 'rbf']
}

gs = GridSearchCV(pipe, param_grid, cv=3)
gs.fit(X, y)

print(gs.best_params_)

A {'svc__C': 0.1, 'svc__kernel': 'rbf'}

B {'svc__C': 0.1, 'svc__kernel': 'linear'}

C {'svc__C': 1, 'svc__kernel': 'linear'}

D {'svc__C': 1, 'svc__kernel': 'rbf'}

💡 Hint

Think about which kernel usually performs better on iris dataset with default settings.

The RBF kernel with C=1 generally performs better on the iris dataset than linear kernel or smaller C values, so GridSearchCV selects {'svc__C': 1, 'svc__kernel': 'rbf'}.

❓ Model Choice

intermediate

Choosing the correct pipeline step for text data

You want to build a pipeline for text classification using GridSearchCV. Which pipeline step should you include before the classifier to convert text into numbers?

A CountVectorizer()

B StandardScaler()

C PCA()

D MinMaxScaler()

💡 Hint

Text data needs to be converted into numeric features before classification.

CountVectorizer converts text into a matrix of token counts, which is suitable for text classification pipelines.

❓ Hyperparameter

advanced

Correct hyperparameter name in pipeline for GridSearchCV

Given a pipeline named 'pipe' with steps [('scaler', StandardScaler()), ('clf', RandomForestClassifier())], which is the correct hyperparameter name to tune the number of trees in GridSearchCV?

A 'n_estimators'

B 'pipe__n_estimators'

C 'clf__n_estimators'

D 'scaler__n_estimators'

💡 Hint

In pipelines, hyperparameters are prefixed by the step name and two underscores.

To tune hyperparameters of a step in a pipeline, use 'stepname__parameter'. Here, 'clf' is the step name, so 'clf__n_estimators' is correct.

❓ Metrics

advanced

Evaluating GridSearchCV best score attribute

After fitting GridSearchCV with cv=5, what does the attribute 'best_score_' represent?

A The training score of the best estimator on the full training data

B The mean cross-validation score of the best estimator across the 5 folds

C The test score on unseen data

D The highest score from a single fold during cross-validation

💡 Hint

GridSearchCV uses cross-validation to estimate performance.

'best_score_' is the average cross-validation score for the best parameter set found during GridSearchCV.

🔧 Debug

expert

Identifying error in pipeline parameter grid

What error will the following GridSearchCV code raise? from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV pipe = Pipeline([ ('scaler', StandardScaler()), ('logreg', LogisticRegression()) ]) param_grid = { 'C': [0.1, 1, 10] } gs = GridSearchCV(pipe, param_grid) gs.fit(X, y)

A KeyError because 'C' is not a valid parameter name for the pipeline

B TypeError because LogisticRegression() is missing required arguments

C ValueError because param_grid is empty

D No error, code runs successfully

💡 Hint

In pipelines, hyperparameters must be prefixed by the step name.

The param_grid key 'C' is invalid because it lacks the step prefix 'logreg__'. This causes a KeyError during GridSearchCV.

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