Which code snippet can be used to fill in the missing lines of code to train the SVM classifier, predict the test set results, and print the classification report?from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import classification_report# Generate a synthetic datasetX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise the SVM classifier with a radial basis function kernelsvm_rbf = SVC(kernel='rbf')# Fit the classifier to the training data# [Your Code Here] - Line to add for fitting the model# Predict the test set results# [Your Code Here] - Line to add for making predictions# Generate and print the classification report# [Your Code Here] - Line to add for printing the classification report
Question
Which code snippet can be used to fill in the missing lines of code to train the SVM classifier, predict the test set results, and print the classification report?from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import classification_report# Generate a synthetic datasetX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise the SVM classifier with a radial basis function kernelsvm_rbf = SVC(kernel='rbf')# Fit the classifier to the training data# [Your Code Here] - Line to add for fitting the model# Predict the test set results# [Your Code Here] - Line to add for making predictions# Generate and print the classification report# [Your Code Here] - Line to add for printing the classification report
Solution
Here is the code snippet that can be used to fill in the missing lines of code:
# Fit the classifier to the training data
svm_rbf.fit(X_train, y_train)
# Predict the test set results
y_pred = svm_rbf.predict(X_test)
# Generate and print the classification report
report = classification_report(y_test, y_pred)
print(report)
So, the complete code will look like this:
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# Generate a synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Initialise the SVM classifier with a radial basis function kernel
svm_rbf = SVC(kernel='rbf')
# Fit the classifier to the training data
svm_rbf.fit(X_train, y_train)
# Predict the test set results
y_pred = svm_rbf.predict(X_test)
# Generate and print the classification report
report = classification_report(y_test, y_pred)
print(report)
Similar Questions
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