SciPy with scikit-learn pipeline - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

from sklearn.pipeline import [1]

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

APipeline

Bpipeline

CPipe

DPipelines

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'pipeline' instead of 'Pipeline'.

Trying to import 'Pipe' or 'Pipelines' which do not exist.

The Pipeline class is imported with a capital P as Pipeline from sklearn.pipeline.

2fill in blank

medium

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

SciPy

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

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

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

AMinMaxScaler

BStandardScaler

CNormalizer

DRobustScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using a scaler that does not standardize data like MinMaxScaler.

Forgetting to instantiate the scaler with parentheses.

The pipeline uses StandardScaler() to scale features before logistic regression.

3fill in blank

hard

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

SciPy

pipeline = Pipeline([('scaler', StandardScaler()), ('clf', LogisticRegression())])
pipeline.[1](X_train, y_train)

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

Afit

Btransform

Cpredict

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' which only changes data but does not train.

Using 'predict' before training the model.

To train the pipeline, use the fit method with features and labels.

4fill in blank

hard

Fill both blanks to create a pipeline that scales data and then applies a KNeighborsClassifier.

SciPy

from sklearn.neighbors import [1]
pipeline = Pipeline([('scaler', StandardScaler()), ('knn', [2]())])

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

AKNeighborsClassifier

BLogisticRegression

CRandomForestClassifier

DSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different classifier than the one used in the pipeline.

Using different classifiers in import and pipeline steps.

The KNeighborsClassifier is imported and used as the classifier in the pipeline.

5fill in blank

hard

Fill all three blanks to create a pipeline that scales data, applies PCA for dimensionality reduction, and then fits a logistic regression model.

SciPy

from sklearn.decomposition import [1]
from sklearn.linear_model import [2]
pipeline = Pipeline([('scaler', StandardScaler()), ('pca', [3](n_components=2)), ('clf', LogisticRegression())])

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

APCA

BLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing PCA with other decomposition methods.

Using a different classifier than logistic regression.

The pipeline uses PCA for dimensionality reduction and LogisticRegression as the classifier.

Practice

1. What is the main benefit of using a Pipeline in scikit-learn when combined with SciPy functions?

easy

A. It organizes data processing and modeling steps into one repeatable workflow.

B. It automatically improves model accuracy without tuning.

C. It replaces the need for any data cleaning.

D. It allows running code without importing any libraries.

Solution

1. Step 1: Understand the purpose of Pipeline

   A Pipeline in scikit-learn is designed to chain multiple steps like data transformation and modeling into a single object.

2. Step 2: Recognize the benefit of combining SciPy functions

   Using SciPy functions inside a Pipeline via FunctionTransformer keeps the workflow organized and repeatable.

3. Final Answer:

   It organizes data processing and modeling steps into one repeatable workflow. -> Option A

4. Quick Check:

   Pipeline = Organized workflow [OK]

Hint: Pipelines bundle steps for easy reuse and clarity [OK]

Common Mistakes:

• Thinking Pipeline improves accuracy automatically
• Assuming Pipeline removes need for data cleaning
• Believing Pipeline runs without imports

2. Which of the following is the correct way to include a SciPy function scipy_func inside a scikit-learn pipeline using FunctionTransformer?

easy

A. Pipeline([('transform', scipy_func), ('model', LogisticRegression())])

B. Pipeline([('transform', FunctionTransformer(scipy_func)), ('model', LogisticRegression())])

C. Pipeline([('transform', FunctionTransformer()), ('model', LogisticRegression())])

D. Pipeline([('transform', FunctionTransformer(scipy_func())), ('model', LogisticRegression())])

Solution

1. Step 1: Understand FunctionTransformer usage

   FunctionTransformer takes a function as an argument without calling it (no parentheses).

2. Step 2: Identify correct pipeline syntax

   The pipeline step should be ('transform', FunctionTransformer(scipy_func)) to wrap the function properly.

3. Final Answer:

   Pipeline([('transform', FunctionTransformer(scipy_func)), ('model', LogisticRegression())]) -> Option B

4. Quick Check:

   FunctionTransformer(function) no parentheses [OK]

Hint: Pass function name, not call, to FunctionTransformer [OK]

Common Mistakes:

• Calling the function inside FunctionTransformer
• Passing function directly without FunctionTransformer
• Using FunctionTransformer without function argument

3. What will be the output of the following code snippet?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
import numpy as np

def add_one(X):
    return X + 1

pipe = Pipeline([
    ('add', FunctionTransformer(add_one)),
])

X = np.array([1, 2, 3])
result = pipe.transform(X)
print(result)

medium

A. Error: Pipeline has no transform method

B. [1 2 3]

C. [0 1 2]

D. [2 3 4]

Solution

1. Step 1: Understand FunctionTransformer behavior

   FunctionTransformer applies the function add_one to input data during transform.

2. Step 2: Apply the function to input array

   Input array [1, 2, 3] plus 1 becomes [2, 3, 4].

3. Final Answer:

   [2 3 4] -> Option D

4. Quick Check:

   Input + 1 = Output [OK]

Hint: FunctionTransformer applies function on transform call [OK]

Common Mistakes:

• Assuming pipeline has no transform method
• Forgetting function adds 1
• Confusing fit and transform methods

4. Identify the error in this pipeline code using a SciPy function inside FunctionTransformer:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
import numpy as np

def multiply_by_two(X):
    return X * 2

pipe = Pipeline([
    ('mult', FunctionTransformer(multiply_by_two())),
])

X = np.array([1, 2, 3])
result = pipe.transform(X)
print(result)

medium

A. Using transform instead of fit_transform

B. Missing import for numpy

C. Calling multiply_by_two() instead of passing the function

D. Pipeline missing a model step

Solution

1. Step 1: Check FunctionTransformer argument

   FunctionTransformer expects a function, not the result of a function call.

2. Step 2: Identify the error in code

   Code calls multiply_by_two() immediately, which causes an error because it returns an array, not a function.

3. Final Answer:

   Calling multiply_by_two() instead of passing the function -> Option C

4. Quick Check:

   Pass function, don't call it [OK]

Hint: Pass function name, avoid parentheses in FunctionTransformer [OK]

Common Mistakes:

• Calling function instead of passing it
• Assuming pipeline needs a model step
• Confusing transform with fit_transform

5. You want to build a pipeline that first applies a SciPy function to normalize data, then fits a logistic regression model. Which of the following code snippets correctly implements this?

hard

A. from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LogisticRegression
import scipy.stats as stats

pipe = Pipeline([
('normalize', FunctionTransformer(stats.zscore)),
('model', LogisticRegression())
])

B. from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LogisticRegression
import scipy.stats as stats

pipe = Pipeline([
('normalize', stats.zscore()),
('model', LogisticRegression())
])

C. from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LogisticRegression
import scipy.stats as stats

pipe = Pipeline([
('normalize', FunctionTransformer(stats.zscore())),
('model', LogisticRegression())
])

D. from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LogisticRegression
import scipy.stats as stats

pipe = Pipeline([
('normalize', FunctionTransformer(stats.zscore)),
('model', LogisticRegression)
])

Solution

1. Step 1: Use FunctionTransformer correctly with SciPy function

   Pass the function stats.zscore without calling it, wrapped by FunctionTransformer.

2. Step 2: Ensure LogisticRegression is instantiated

   Use LogisticRegression() with parentheses to create the model instance.

3. Final Answer:

   Code snippet with FunctionTransformer(stats.zscore) and LogisticRegression() -> Option A

4. Quick Check:

   FunctionTransformer(function) + model instance [OK]

Hint: Wrap function, instantiate model with parentheses [OK]

Common Mistakes:

• Calling SciPy function instead of passing it
• Not instantiating LogisticRegression
• Passing function call to FunctionTransformer