CODEWITHSUNDEEP
pythonpandasnumpyscikit-learn
mk2080
mk2080

Reputation: 922

Classification Model: How to check the score of each classification

I'm using RandomForestClassifier to categorize my data into 2 types -- 0 or 1. Currently I am using the below code and get the overall score of all of the testing data. What I'd like to do is get the seperate scores for type 0 data and type 1 data. Any help appreciated!

X = features_enc
Y = np.asarray(df[target_column])


x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)

random.seed(100)
rf = RandomForestClassifier(n_estimators=100)
rf.fit(x_train, y_train)

score = rf.score(x_test, y_test)

print(score)

Upvotes: 1

Views: 149

Answers (1)

Sergey Ronin
Sergey Ronin

Reputation: 776

You can import the following from sklearn:

from sklearn.metrics import classification_report

This will provide you with all possible scores:

# --snip--

predicted = rf.predict(x_test)
print(classification_report(y_test, predicted))

This should print a nicely formatted evaluation. See docs for further info

Upvotes: 3

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