scikit-learn Pipeline in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple scikit-learn Pipeline

What is the output of the following code snippet when predicting with the pipeline?

ML Python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import numpy as np

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression(random_state=0))
])

X_train = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
y_train = np.array([0, 0, 1, 1])

pipeline.fit(X_train, y_train)

X_test = np.array([[1.5, 2.5]])
prediction = pipeline.predict(X_test)
print(prediction)

A [0 1]

B [0]

C [1]

D Raises a ValueError

💡 Hint

Think about how the pipeline transforms data before prediction.

The pipeline first scales the input using StandardScaler, then applies LogisticRegression. Given the training data, the model predicts class 1 for the test input.

❓ Model Choice

intermediate

Choosing the correct pipeline step for text data

You want to build a pipeline to classify text documents. Which step should you include before the classifier to convert text into numbers?

A KMeans()

B CountVectorizer()

C PCA()

D StandardScaler()

💡 Hint

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

CountVectorizer converts text documents into a matrix of token counts, which can be used by classifiers.

❓ Hyperparameter

advanced

Setting hyperparameters in a pipeline

Given this pipeline: pipeline = Pipeline([ ('scaler', StandardScaler()), ('clf', LogisticRegression()) ]) How do you set the LogisticRegression parameter 'C' to 0.5 when using GridSearchCV?

A {'scaler__C': [0.5]}

B {'C': [0.5]}

C {'pipeline__C': [0.5]}

D {'clf__C': [0.5]}

💡 Hint

Use double underscores to access parameters of steps inside the pipeline.

Parameters of steps in a pipeline are accessed by 'stepname__parameter'. Here, 'clf' is the step name.

❓ Metrics

advanced

Evaluating pipeline performance with cross-validation

You run cross_val_score on a pipeline with a classifier and get these scores: [0.8, 0.85, 0.78, 0.82, 0.81]. What is the mean accuracy?

A 0.81

B 0.82

C 0.80

D 0.83

💡 Hint

Add all scores and divide by the number of scores.

Mean accuracy = (0.8 + 0.85 + 0.78 + 0.82 + 0.81) / 5 = 0.812, rounded to 0.81.

🔧 Debug

expert

Identifying error in pipeline usage

What error does this code raise? from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression pipeline = Pipeline([ ('scaler', StandardScaler()), ('clf', LogisticRegression()) ]) X_test = [[1, 2], [3, 4]] prediction = pipeline.predict(X_test)

A ValueError: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

B AttributeError: 'Pipeline' object has no attribute 'predict'

C TypeError: 'list' object is not callable

D No error, outputs predictions

💡 Hint

You must train the pipeline before predicting.

The pipeline is not fitted before calling predict, so it raises a ValueError.

Practice

1. What is the main purpose of using a Pipeline in scikit-learn?

easy

A. To manually split data into training and testing sets

B. To chain preprocessing steps and model training into one object

C. To visualize the data distribution

D. To increase the size of the dataset

Solution

1. Step 1: Understand what a Pipeline does

   A Pipeline in scikit-learn combines multiple steps like data preprocessing and model training into a single object.

2. Step 2: Identify the main purpose

   This chaining helps keep code clean and allows fitting and predicting in one call.

3. Final Answer:

   To chain preprocessing steps and model training into one object -> Option B

4. Quick Check:

   Pipeline = chaining steps [OK]

Hint: Pipeline chains steps for clean, safe model building [OK]

Common Mistakes:

• Thinking Pipeline is for data visualization
• Confusing Pipeline with data splitting
• Assuming Pipeline increases data size

2. Which of the following is the correct way to create a scikit-learn Pipeline with a scaler and a logistic regression model?

easy

A. Pipeline(('scaler', StandardScaler()), ('model', LogisticRegression()))

B. Pipeline({'scaler': StandardScaler(), 'model': LogisticRegression()})

C. Pipeline([('scaler', StandardScaler()), ('model', LogisticRegression())])

D. Pipeline(['scaler': StandardScaler(), 'model': LogisticRegression()])

Solution

1. Step 1: Recall Pipeline syntax

   A Pipeline requires a list of tuples, each tuple with a name and a transformer or estimator.

2. Step 2: Check each option

   Pipeline([('scaler', StandardScaler()), ('model', LogisticRegression())]) uses a list of tuples correctly. Options B and D use dictionary syntax which is invalid. Pipeline(('scaler', StandardScaler()), ('model', LogisticRegression())) uses tuples but not inside a list.

3. Final Answer:

   Pipeline([('scaler', StandardScaler()), ('model', LogisticRegression())]) -> Option C

4. Quick Check:

   Pipeline needs list of (name, step) tuples [OK]

Hint: Use list of (name, step) tuples to build Pipeline [OK]

Common Mistakes:

• Using dictionary instead of list of tuples
• Passing tuples without list
• Using incorrect brackets or colons

3. Given the code below, what will print(y_pred) output?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import numpy as np

X_train = np.array([[1, 2], [2, 3], [3, 4]])
y_train = np.array([0, 1, 0])
X_test = np.array([[1, 2], [4, 5]])

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression())
])
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
print(y_pred)

medium

A. [1 0]

B. [1 1]

C. [0 0]

D. [0 1]

Solution

1. Step 1: Understand the pipeline steps

   The pipeline first scales the data, then fits LogisticRegression on training data.

2. Step 2: Predict on test data

   After scaling, the model predicts labels for X_test. Given training labels, the model likely predicts 0 for [1,2] and 1 for [4,5].

3. Final Answer:

   [0 1] -> Option D

4. Quick Check:

   Scaled data + logistic regression predicts [0 1] [OK]

Hint: Pipeline applies all steps in order before predict [OK]

Common Mistakes:

• Ignoring scaling effect on prediction
• Assuming model predicts all zeros
• Confusing training and test labels

4. What is wrong with the following Pipeline code?

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

pipe = Pipeline([
    ('scaler', StandardScaler),
    ('model', LogisticRegression())
])
pipe.fit(X_train, y_train)

medium

A. StandardScaler is not instantiated with parentheses

B. LogisticRegression should be imported from sklearn.svm

C. Pipeline requires a dictionary, not a list

D. fit method is missing required parameters

Solution

1. Step 1: Check each pipeline step

   StandardScaler is passed without parentheses, so it is the class, not an instance.

2. Step 2: Understand Pipeline requirements

   Pipeline steps must be instances, so StandardScaler() is needed. LogisticRegression() is correct.

3. Final Answer:

   StandardScaler is not instantiated with parentheses -> Option A

4. Quick Check:

   Instantiate transformers with () [OK]

Hint: Always instantiate transformers with parentheses in Pipeline [OK]

Common Mistakes:

• Passing classes instead of instances
• Wrong import for LogisticRegression
• Using dict instead of list for Pipeline steps

5. You want to build a Pipeline that first fills missing values with the mean, then scales features, and finally trains a RandomForestClassifier. Which of the following Pipeline definitions is correct?

hard

A. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()), ('model', RandomForestClassifier())])

B. Pipeline([('scaler', StandardScaler()), ('imputer', SimpleImputer(strategy='mean')), ('model', RandomForestClassifier())])

C. Pipeline([('model', RandomForestClassifier()), ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())])

D. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('model', RandomForestClassifier()), ('scaler', StandardScaler())])

Solution

1. Step 1: Determine correct order of steps

   Missing values must be filled first, then scaling, then model training.

2. Step 2: Check each option's order

   Pipeline([('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()), ('model', RandomForestClassifier())]) follows the correct order: imputer, scaler, model. Others have wrong order.

3. Final Answer:

   Pipeline([('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()), ('model', RandomForestClassifier())]) -> Option A

4. Quick Check:

   Impute -> scale -> model [OK]

Hint: Impute missing -> scale features -> train model [OK]

Common Mistakes:

• Scaling before imputing missing values
• Placing model before preprocessing steps
• Incorrect step order causing errors