SVM for text classification in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SVM classifier from scikit-learn.

NLP

from sklearn.[1] import SVC

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

Alinear_model

Bneighbors

Censemble

Dsvm

Attempts:

3 left

💡 Hint

Common Mistakes

Importing from the wrong module like linear_model or ensemble.

Trying to import SVC directly from sklearn.

The SVM classifier is in the svm module of scikit-learn.

2fill in blank

medium

Complete the code to create an SVM classifier with a linear kernel.

NLP

model = SVC(kernel=[1])

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

A'linear'

B'rbf'

C'poly'

D'sigmoid'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rbf' or 'poly' which are other kernel types.

Forgetting the quotes around the kernel name.

The linear kernel is specified by the string 'linear'.

3fill in blank

hard

Fix the error in the code to fit the SVM model on training data.

NLP

model.[1](X_train, y_train)

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

Atrain

Btransform

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a method.

Using 'predict' which is for making predictions, not training.

The method to train the model is fit.

4fill in blank

hard

Fill both blanks to create a dictionary of predictions for each test sample and calculate accuracy.

NLP

predictions = model.[1](X_test)
accuracy = accuracy_score(y_test, [2])

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

Apredict

Bpredictions

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'predict' for predictions.

Passing the wrong variable to accuracy_score.

Use predict to get predictions, then pass them to accuracy_score to compute accuracy.

5fill in blank

hard

Fill all three blanks to create a TF-IDF vectorizer, transform text data, and train the SVM model.

NLP

vectorizer = TfidfVectorizer([1]=1, [2]=0.9)
X_train_tfidf = vectorizer.[3](train_texts)
model.fit(X_train_tfidf, train_labels)

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

Amin_df

Bfit_transform

Cmax_df

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of fit_transform on training data.

Mixing up min_df and max_df parameters.

min_df and max_df set frequency thresholds for words. fit_transform learns vocabulary and transforms texts.

Practice

1. What is the main purpose of using an SVM (Support Vector Machine) in text classification?

easy

A. To find the best line that separates different text categories

B. To count the number of words in the text

C. To translate text into another language

D. To generate random text samples

Solution

1. Step 1: Understand SVM's role in classification

   SVM tries to find a boundary (line or hyperplane) that best separates different classes in data.

2. Step 2: Apply this to text classification

   In text classification, SVM finds the best line to separate categories like spam vs. not spam.

3. Final Answer:

   To find the best line that separates different text categories -> Option A

4. Quick Check:

   SVM separates classes = D [OK]

Hint: SVM separates classes by finding the best boundary line [OK]

Common Mistakes:

• Thinking SVM counts words directly
• Confusing SVM with translation tools
• Assuming SVM generates text

2. Which of the following is the correct way to convert text data before applying an SVM model in Python?

easy

A. Use CountVectorizer() or TfidfVectorizer() to transform text into numbers

B. Directly feed raw text strings into the SVM model

C. Use OneHotEncoder() on raw text strings

D. Apply StandardScaler() on raw text strings

Solution

1. Step 1: Identify text preprocessing for SVM

   SVM requires numeric input, so text must be converted to numbers using vectorizers like CountVectorizer or TfidfVectorizer.

2. Step 2: Check other options

   Raw text cannot be fed directly; OneHotEncoder and StandardScaler are not suitable for raw text strings.

3. Final Answer:

   Use CountVectorizer() or TfidfVectorizer() to transform text into numbers -> Option A

4. Quick Check:

   Text to numbers = Vectorizer = C [OK]

Hint: Always vectorize text before SVM, never raw strings [OK]

Common Mistakes:

• Feeding raw text directly to SVM
• Using OneHotEncoder on text strings
• Applying scalers on text without vectorizing

3. Given the following Python code snippet, what will be the output of print(predicted_labels)?

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

texts = ["I love cats", "Dogs are great", "Cats are cute", "I hate dogs"]
labels = [1, 0, 1, 0]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)

model = LinearSVC()
model.fit(X, labels)

new_texts = ["I love dogs", "Cats are great"]
X_new = vectorizer.transform(new_texts)
predicted_labels = model.predict(X_new)

medium

A. [1, 0]

B. [0, 1]

C. [1, 1]

D. [0, 0]

Solution

1. Step 1: Understand training labels and texts

   Texts labeled 1 are about cats, 0 about dogs. Model learns cats=1, dogs=0.

2. Step 2: Predict new texts

   "I love dogs" likely labeled 0 (dog), "Cats are great" labeled 1 (cat).

3. Final Answer:

   [0, 1] -> Option B

4. Quick Check:

   Dog text=0, Cat text=1 = B [OK]

Hint: Match new text topics to training labels for quick guess [OK]

Common Mistakes:

• Mixing label meanings
• Assuming model predicts opposite labels
• Ignoring vectorizer effect

4. You trained an SVM model for text classification but got an error: ValueError: could not convert string to float. What is the most likely cause?

medium

A. You set the wrong kernel parameter in SVM

B. You used too many training samples

C. You forgot to convert text data into numeric vectors before training

D. You used a linear kernel instead of RBF kernel

Solution

1. Step 1: Analyze the error message

   The error means the model received raw text strings instead of numbers.

2. Step 2: Identify cause in text classification

   Text must be vectorized (converted to numbers) before training SVM.

3. Final Answer:

   You forgot to convert text data into numeric vectors before training -> Option C

4. Quick Check:

   Raw text input causes conversion error = A [OK]

Hint: Check if text is vectorized before training SVM [OK]

Common Mistakes:

• Ignoring need for vectorization
• Blaming kernel choice for conversion errors
• Assuming data size causes this error

5. You want to improve your SVM text classifier's performance on a dataset with many common words like "the", "and", "is". Which approach is best to try?

hard

A. Switch to a polynomial kernel without changing text preprocessing

B. Increase the SVM regularization parameter without changing vectorization

C. Use raw word counts without removing stop words

D. Use a TF-IDF vectorizer to reduce the impact of common words

Solution

1. Step 1: Understand the problem with common words

   Common words appear everywhere and do not help distinguish classes well.

2. Step 2: Choose vectorization method to reduce common word impact

   TF-IDF lowers weights of common words, improving model focus on important words.

3. Step 3: Evaluate other options

   Changing regularization or kernel without addressing common words won't help much.

4. Final Answer:

   Use a TF-IDF vectorizer to reduce the impact of common words -> Option D

5. Quick Check:

   TF-IDF reduces common word weight = A [OK]

Hint: TF-IDF downweights common words, improving text classification [OK]

Common Mistakes:

• Ignoring stop words effect
• Changing SVM parameters without vectorizing
• Using raw counts with many common words