scikit-learn Pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Pipeline class from scikit-learn.

ML Python

from sklearn.pipeline import [1]

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

APipeline

Bpipe

CPipeLine

Dpipeline

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'pipeline' instead of 'Pipeline'.

Misspelling the class name with wrong capitalization.

The correct class name is Pipeline with a capital P.

2fill in blank

medium

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

ML Python

pipeline = Pipeline(steps=[('scaler', [1]()), ('model', LogisticRegression())])

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

AstandardScaler

BScalerStandard

CStandardScaler

Dscale

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect class names like 'ScalerStandard' or lowercase variants.

Forgetting to import StandardScaler before using it.

The correct scaler class is StandardScaler from scikit-learn.

3fill in blank

hard

Fix the error in the code to fit the pipeline on training data X_train and y_train.

ML Python

pipeline.[1](X_train, y_train)

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

Afit

Bfit_transform

Ctransform

Dtrain

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a method in scikit-learn.

Using 'transform' which does not accept labels.

The method to train the pipeline is fit.

4fill in blank

hard

Fill both blanks to create a pipeline that first applies PCA and then a classifier.

ML Python

pipeline = Pipeline(steps=[('pca', [1](n_components=2)), ('clf', [2]())])

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

APCA

BLogisticRegression

CRandomForestClassifier

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler instead of PCA for the first step.

Using LogisticRegression when the question expects RandomForestClassifier.

The first step is PCA for dimensionality reduction, and the second is RandomForestClassifier as the model.

5fill in blank

hard

Fill all three blanks to create a pipeline that scales data, reduces dimensions, and classifies.

ML Python

pipeline = Pipeline(steps=[('scale', [1]()), ('reduce', [2](n_components=3)), ('classify', [3]())])

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

AStandardScaler

BPCA

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of steps in the pipeline.

Using KNeighborsClassifier instead of LogisticRegression as classifier.

The pipeline first scales data with StandardScaler, then reduces dimensions with PCA, and finally classifies with LogisticRegression.

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