t-SNE for visualization in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the t-SNE class from scikit-learn.

ML Python

from sklearn.manifold import [1]

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ATSNE

BtSNE

Ctsne

DTSne

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or incorrect capitalization for TSNE.

Trying to import from sklearn.cluster instead of sklearn.manifold.

The correct class name for t-SNE in scikit-learn is TSNE with uppercase letters as shown.

2fill in blank

medium

Complete the code to create a t-SNE object with 2 output dimensions.

ML Python

tsne = TSNE(n_components=[1])

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A1

B3

C0

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting n_components to 3 or 1 which are not typical for 2D visualization.

Using 0 which is invalid.

For visualization, t-SNE is usually set to 2 components to reduce data to 2D.

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hard

Fix the error in the code to fit and transform data using t-SNE.

ML Python

X_embedded = tsne.[1](X)

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Afit

Bfit_transform

Ctransform

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() alone which does not return transformed data.

Using transform() which is not supported by t-SNE.

Using fit_predict() which is not a method of TSNE.

t-SNE requires fit_transform to both fit the model and get the transformed data in one step.

4fill in blank

hard

Fill both blanks to create a scatter plot of the t-SNE results with colors.

ML Python

plt.scatter(X_embedded[:, [1]], X_embedded[:, [2]], c=labels, cmap='viridis')

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A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using indices 1 and 2 which may cause index errors.

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

For 2D t-SNE output, the first dimension is column 0 and the second is column 1.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each label to the count of points with that label.

ML Python

label_counts = {label: sum(1 for x in labels if x [1] label) for label in set(labels) if label [2] 0 and label [3] -1}

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A==

B>

C!=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators causing logic errors.

Confusing != and == in conditions.

The comprehension counts points where label equals the current label (==), filters labels greater than 0 (>), and excludes label -1 (!=).