Logistic regression for text in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the logistic regression model from scikit-learn.

NLP

from sklearn.linear_model import [1]

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

ALogisticRegression

BLinearRegression

CDecisionTreeClassifier

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing LinearRegression which is for regression, not classification.

Confusing with other classifiers like DecisionTreeClassifier.

The logistic regression model class in scikit-learn is called LogisticRegression. It is used for classification tasks.

2fill in blank

medium

Complete the code to convert text data into numerical features using CountVectorizer.

NLP

from sklearn.feature_extraction.text import [1]

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

ACountVectorizer

BDictVectorizer

CTfidfVectorizer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfVectorizer when the task asks specifically for count-based features.

Using LabelEncoder which is for labels, not text features.

CountVectorizer converts text documents into a matrix of token counts, which is suitable for logistic regression input.

3fill in blank

hard

Fix the error in the code to train the logistic regression model on vectorized text data.

NLP

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

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

Apredict

Bscore

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit causes errors because the model is not trained yet.

Using transform which is a method for vectorizers, not models.

The fit method trains the model using the training features and labels.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their counts only if the count is greater than 2.

NLP

word_counts = {word: [1] for word, count in counts.items() if count [2] 2}

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

Acount

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word as the value instead of count.

Using < instead of > in the condition.

The dictionary comprehension keeps words with counts greater than 2, so the value is count and the condition is >.

5fill in blank

hard

Fill all three blanks to create a dictionary of word lengths for words longer than 4 characters.

NLP

lengths = [1]: [2] for [3] in words if len([3]) > 4}

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using item as the loop variable but not matching keys and values.

Using len(item) without defining item.

The dictionary keys are words, values are their lengths, and the loop variable is word.