Gaussian Mixture Models in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the GaussianMixture class from sklearn.

ML Python

from sklearn.mixture import [1]

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AGaussianMixture

BKMeans

CLinearRegression

DDecisionTreeClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing KMeans instead of GaussianMixture.

Using a classifier class instead of a mixture model.

The GaussianMixture class is used to create Gaussian Mixture Models in scikit-learn.

2fill in blank

medium

Complete the code to create a Gaussian Mixture Model with 3 components.

ML Python

model = GaussianMixture(n_components=[1])

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A5

B3

C1

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting n_components to 1 when multiple clusters are expected.

Using too many components without reason.

The n_components parameter sets how many Gaussian distributions the model will use. Here, 3 is correct.

3fill in blank

hard

Fix the error in the code to fit the model to data stored in X.

ML Python

model.[1](X)

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Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before fitting the model.

Using transform instead of fit.

The fit method trains the Gaussian Mixture Model on the data X.

4fill in blank

hard

Fill both blanks to predict the cluster labels for data X.

ML Python

labels = model.[1](X)
print(labels.[2])

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Apredict

Bshape

Csize

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict to get labels.

Using size instead of shape to check label array dimensions.

Use predict to get cluster labels, then shape to see the number of labels.

5fill in blank

hard

Fill all three blanks to compute the probability of each sample belonging to each cluster.

ML Python

probabilities = model.[1](X)
max_probs = probabilities.max(axis=[2])
print(max_probs.[3]())

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Apredict_proba

B1

Cmean

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of predict_proba for probabilities.

Using axis=0 instead of axis=1 in max.

Using size instead of mean to summarize probabilities.

predict_proba returns probabilities per cluster, max with axis=1 finds max per sample, mean computes average max probability.