Multi-class text classification in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NLP

from sklearn.feature_extraction.text import [1]

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ACountVectorizer

BLabelEncoder

Ctrain_test_split

DTfidfVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'CountVectorizer' which counts word occurrences but doesn't weigh them.

Selecting 'LabelEncoder' which is for encoding labels, not text features.

The TfidfVectorizer converts text data into numerical features using TF-IDF scores, which is common in text classification.

2fill in blank

medium

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

NLP

from sklearn.model_selection import [1]
X_train, X_test, y_train, y_test = [1](X, y, test_size=0.2, random_state=42)

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Atrain_test_split

Bcross_val_score

CGridSearchCV

DKFold

Attempts:

3 left

💡 Hint

Common Mistakes

Using cross-validation functions instead of splitting data.

Confusing with KFold which is for cross-validation, not simple splitting.

train_test_split is used to split data into training and testing sets for model evaluation.

3fill in blank

hard

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

NLP

from sklearn.linear_model import [1]
model = [1]()
model.fit(X_train, y_train)

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ALinearRegression

BLogisticRegression

CKNeighborsClassifier

DDecisionTreeRegressor

Attempts:

3 left

💡 Hint

Common Mistakes

Using regression models for classification tasks.

Choosing classifiers that are not imported or mismatched.

LogisticRegression is suitable for classification tasks, including multi-class classification.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for words longer than 3 characters.

NLP

word_counts = {word: [1] for word in text.split() if len(word) [2] 3}

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Atext.count(word)

B>

C>=

Dlen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator like '<' or '<='.

Using the length of the word as the value instead of the count.

The dictionary comprehension counts occurrences of words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary of words and their counts for words longer than 4 characters.

NLP

filtered_counts = [1]{word: [2] for word in words if len(word) [3] 4}

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Adict(

Bwords.count(word)

C>

Dlist(

Attempts:

3 left

💡 Hint

Common Mistakes

Using < or <= instead of > for filtering word length.

Using list() instead of dict() to create the dictionary.

The code builds a dictionary from a comprehension, counting words longer than 4 characters.