CODEWITHSUNDEEP
pythonscikit-learn
dev007
dev007

Reputation: 49

How to get precision and recall using Linear svc of SVM?

I have used SVM's Linear svc for training and testing the data. I'm able to get the accuracy for SVM on my dataset. But, in addition to accuracy, I need precision and recall. Can anyone suggest me how to calculate precision and recall.

MyCode:

from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
with open("/Users/abc/Desktop/reviews.txt") as f:
    reviews = f.read().split("\n")
with open("/Users/abc/Desktop/labels.txt") as f:
    labels = f.read().split("\n")

reviews_tokens = [review.split() for review in reviews]


onehot_enc = MultiLabelBinarizer()
onehot_enc.fit(reviews_tokens)


X_train, X_test, y_train, y_test = train_test_split(reviews_tokens, labels, test_size=0.20, random_state=None)

lsvm = LinearSVC()
lsvm.fit(onehot_enc.transform(X_train), y_train)
score = lsvm.score(onehot_enc.transform(X_test), y_test)
print("Score of SVM:" , score)

Upvotes: 0

Views: 2947

Answers (1)

Kalsi
Kalsi

Reputation: 583

You can do like this:

from sklearn.metrics import confusion_matrix

predicted_y = lsvm.predict(X_test)
tn, fp, fn, tp = confusion_matrix(y_test, predicted_y).ravel()
precision_score = tp / (tp + fp)
recall_score = tp / (tp + fn)

Refer confusion_matrix documentation for more info

Upvotes: 2

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