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

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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])

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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)

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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])

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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.