Why advanced techniques handle complex data in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the machine learning library.

ML Python

import [1]

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

Asklearn

Btensorflow

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of sklearn

Importing matplotlib which is for plotting

Importing tensorflow which is for deep learning but not the simplest here

We use sklearn (scikit-learn) for many machine learning tasks because it provides easy-to-use tools for advanced techniques.

2fill in blank

medium

Complete the code to create a decision tree classifier.

ML Python

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier([1]=42)

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Alearning_rate

Bmax_depth

Cn_estimators

Drandom_state

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_depth which limits tree size but is not for randomness

Using n_estimators which is for ensemble methods

Using learning_rate which is for boosting methods

The random_state parameter ensures reproducible results by fixing the random seed.

3fill in blank

hard

Fix the error in the code to train the model on data X and labels y.

ML Python

model.fit([1], y)

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AX

Bfit

Cy

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping X and y arguments

Passing the model itself as data

Passing the method name instead of data

The fit method requires the input features X first, then the labels y.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

ML Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length

Using less than instead of greater than in condition

We want the length of each word, so len(word) is the value. We filter words longer than 3 letters using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 2.

ML Python

filtered = [1]: [2] for [3], [2] in data.items() if [2] > 2

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Ak.upper()

Bv

Ck

Ditems

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'items' as loop variable instead of key

Not converting keys to uppercase

Using wrong variable names

The keys are converted to uppercase with k.upper(), values are v, and we loop using k, v from data.items().