Text classification pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary module for text vectorization.

ML Python

from sklearn.feature_extraction.text import [1]

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ATfidfVectorizer

Btrain_test_split

CLabelEncoder

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing CountVectorizer which counts words but doesn't weigh them.

Choosing LabelEncoder which is for labels, not text features.

Choosing train_test_split which is for splitting data.

TfidfVectorizer converts text to numerical features based on term frequency and inverse document frequency, which is common in text classification.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets.

ML Python

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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A0.2

B0.5

C0.1

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 which means all data is test set.

Using 0.5 which splits data evenly, less common for training.

Using 0.1 which is too small for testing.

A test size of 0.2 means 20% of data is used for testing, which is a common practice.

3fill in blank

hard

Fix the error in the model training code by filling the blank with the correct method name.

ML Python

model = LogisticRegression()
model.[1](X_train, y_train)

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Atrain

Bpredict

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a method in sklearn models.

Using 'fit_transform' which is for transformers, not models.

Using 'predict' which is for prediction, not training.

The 'fit' method trains the model on the training data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

ML Python

{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 '<' instead of '>' which would select shorter words.

Using 'word' as value instead of its length.

Using 'len(word)' in the condition but not as value.

We want the length of each word as the value, and only include words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than 1.

ML Python

{ [1]: [2] for [3] in word_counts if word_counts[[3]] > 1 }

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Aword.upper()

Bword_counts[word]

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word.lower()' instead of uppercase.

Using the word itself as value instead of its count.

Using a variable name not in the loop.

We map the uppercase version of each word to its count, iterating over words in word_counts, filtering counts greater than 1.