Dimensionality reduction visualization in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for dimensionality reduction visualization.

ML Python

from sklearn.manifold import [1]

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

APCA

BTSNE

CKMeans

DLinearRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing PCA which is for linear dimensionality reduction but not imported from manifold.

Choosing KMeans which is a clustering algorithm.

Choosing LinearRegression which is for prediction.

TSNE is a popular method for visualizing high-dimensional data in 2 or 3 dimensions.

2fill in blank

medium

Complete the code to create a TSNE object with 2 components for visualization.

ML Python

tsne = TSNE(n_components=[1], random_state=42)

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

A2

B3

C10

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3 which is for 3D visualization but less common.

Using 10 which is too many for visualization.

Using 1 which is only one dimension, not enough for a scatter plot.

For visualization, we usually reduce data to 2 dimensions.

3fill in blank

hard

Fix the error in the code to fit and transform the data using TSNE.

ML Python

X_embedded = tsne.[1](X)

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

Afit_transform

Btransform

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit alone which does not return transformed data.

Using transform alone which is not available for TSNE.

Using predict which is for supervised models.

TSNE uses fit_transform to both learn and reduce dimensions in one step.

4fill in blank

hard

Fill both blanks to create a scatter plot of the 2D TSNE results with labels.

ML Python

plt.scatter(X_embedded[:, [1]], X_embedded[:, [2]], c=labels, cmap='viridis')
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
plt.title('TSNE Visualization')
plt.colorbar()

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 and 2 which would cause index errors.

Using 2 and 3 which are out of range for 2D data.

For 2D data, the first dimension is index 0 and the second is index 1.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each label to its count in the dataset.

ML Python

label_counts = [1] [2] for [3] in set(labels)}

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

A{label

Blabels.count(label)

Clabel

D[label

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets instead of curly braces for dictionary.

Using variable names that don't match the loop variable.

Not using the count method to get label frequencies.

This dictionary comprehension counts how many times each label appears.

Practice

1. What is the main purpose of dimensionality reduction in data visualization?

easy

A. To simplify complex data into fewer dimensions for easier understanding

B. To increase the number of features in the dataset

C. To remove all data points that are outliers

D. To convert numerical data into text format

Solution

1. Step 1: Understand dimensionality reduction goal

   Dimensionality reduction aims to reduce many features into fewer ones while keeping important information.

2. Step 2: Connect to visualization purpose

   Reducing dimensions helps us visualize data in 2D or 3D, making it easier to see patterns.

3. Final Answer:

   To simplify complex data into fewer dimensions for easier understanding -> Option A

4. Quick Check:

   Dimensionality reduction = simplify data [OK]

Hint: Dimensionality reduction means fewer features to see data clearly [OK]

Common Mistakes:

• Thinking it increases features instead of reducing
• Confusing it with data cleaning or outlier removal
• Believing it changes data type to text

2. Which of the following is the correct way to import PCA from scikit-learn in Python?

easy

A. import PCA from sklearn.decomposition

B. from sklearn.decomposition import PCA

C. from sklearn import PCA

D. import decomposition.PCA from sklearn

Solution

1. Step 1: Recall correct Python import syntax

   Python imports use 'from module import class' format for specific classes.

2. Step 2: Match with scikit-learn PCA import

   PCA is in sklearn.decomposition, so correct import is 'from sklearn.decomposition import PCA'.

3. Final Answer:

   from sklearn.decomposition import PCA -> Option B

4. Quick Check:

   Correct import syntax = from sklearn.decomposition import PCA [OK]

Hint: Use 'from module import class' for specific imports [OK]

Common Mistakes:

• Using wrong import order or syntax
• Trying to import PCA directly from sklearn
• Using 'import' with 'from' incorrectly

3. Given this Python code using PCA, what will be the shape of X_pca?

from sklearn.decomposition import PCA
import numpy as np
X = np.random.rand(100, 10)
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

medium

A. (100, 2)

B. (2, 10)

C. (100, 10)

D. (10, 2)

Solution

1. Step 1: Understand input data shape

   X has 100 samples and 10 features, so shape is (100, 10).

2. Step 2: Apply PCA with n_components=2

   PCA reduces features from 10 to 2, keeping sample count same, so output shape is (100, 2).

3. Final Answer:

   (100, 2) -> Option A

4. Quick Check:

   Samples stay same, features reduced = (100, 2) [OK]

Hint: Output shape = (samples, n_components) after PCA [OK]

Common Mistakes:

• Confusing samples and features dimensions
• Thinking PCA changes number of samples
• Mixing up rows and columns in shape

4. This code tries to visualize data with t-SNE but throws an error. What is the likely cause?

from sklearn.manifold import TSNE
import numpy as np
X = np.random.rand(50, 5)
tsne = TSNE(n_components=3)
X_embedded = tsne.fit_transform(X)

medium

A. t-SNE does not support n_components greater than 2

B. t-SNE requires integer input data

C. Input data must have at least 10 features

D. t-SNE supports n_components=3, so no error here

Solution

1. Step 1: Check t-SNE n_components support

   t-SNE supports 2 or 3 components for visualization, so n_components=3 is valid.

2. Step 2: Verify input data and types

   Input X is float data with shape (50, 5), which is acceptable for t-SNE.

3. Final Answer:

   t-SNE supports n_components=3, so no error here -> Option D

4. Quick Check:

   t-SNE allows 3D output [OK]

Hint: t-SNE supports 2D or 3D outputs [OK]

Common Mistakes:

• Assuming t-SNE only supports 2D
• Thinking input must have many features
• Believing t-SNE needs integer data

5. You have a dataset with 1000 features and want to visualize clusters clearly. Which approach is best?

hard

A. Use PCA with 2 components because it is fast but may miss clusters

B. Use raw data without reduction for best cluster visualization

C. Use t-SNE with 2 components because it shows clusters well despite being slower

D. Use PCA with 100 components to keep most information

Solution

1. Step 1: Consider dataset size and visualization goal

   With 1000 features, dimensionality reduction is needed to visualize clusters clearly.

2. Step 2: Compare PCA and t-SNE for cluster visualization

   PCA is fast but focuses on variance, may miss clusters. t-SNE is slower but better at showing clusters in 2D.

3. Step 3: Choose best method for clear cluster visualization

   t-SNE with 2 components is best to reveal clusters clearly despite slower speed.

4. Final Answer:

   Use t-SNE with 2 components because it shows clusters well despite being slower -> Option C

5. Quick Check:

   t-SNE = better cluster visualization [OK]

Hint: t-SNE reveals clusters better than PCA in 2D [OK]

Common Mistakes:

• Choosing PCA for cluster clarity over t-SNE
• Using too many PCA components for visualization
• Trying to visualize raw high-dimensional data directly