CODEWITHSUNDEEP
pythonpython-3.xscikit-learn
Jancer Lima
Jancer Lima

Reputation: 944

classification_report output with missing accuracy data

I'm taking a course and doing some examples my output comes wrong.

import pandas as pd 

df = pd.read_csv(r'E:\Python Projects\Python-Data-Science-and-Machine-Learning-Bootcamp\Machine Learning\Árvores de decisão e Florestas Aleatórias\kyphosis.csv')

from sklearn.model_selection import train_test_split

x = df.drop('Kyphosis', axis=1)
y = df['Kyphosis']

X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.33)

from sklearn.tree import DecisionTreeClassifier

dtree = DecisionTreeClassifier()
dtree.fit(X_train, y_train)
pred = dtree.predict(X_test)

from sklearn.metrics import classification_report

print(classification_report(y_test, pred))

These 2 data are missing

Upvotes: 3

Views: 5424

Answers (2)

Zabir Al Nazi Nabil
Zabir Al Nazi Nabil

Reputation: 11198

This is how classification_report returns the text summary, nothing is missing.

Look into the documentation: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html

>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
              precision    recall  f1-score   support
<BLANKLINE>
     class 0       0.50      1.00      0.67         1
     class 1       0.00      0.00      0.00         1
     class 2       1.00      0.67      0.80         3
<BLANKLINE>
    accuracy                           0.60         5
   macro avg       0.50      0.56      0.49         5
weighted avg       0.70      0.60      0.61         5
<BLANKLINE>
>>> y_pred = [1, 1, 0]
>>> y_true = [1, 1, 1]
>>> print(classification_report(y_true, y_pred, labels=[1, 2, 3]))
              precision    recall  f1-score   support
<BLANKLINE>
           1       1.00      0.67      0.80         3
           2       0.00      0.00      0.00         0
           3       0.00      0.00      0.00         0
<BLANKLINE>
   micro avg       1.00      0.67      0.80         3
   macro avg       0.33      0.22      0.27         3
weighted avg       1.00      0.67      0.80         3
<BLANKLINE>

The reported averages include macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label), and sample average (only for multilabel classification). Micro average (averaging the total true positives, false negatives and false positives) is only shown for multi-label or multi-class with a subset of classes, because it corresponds to accuracy otherwise.

Your accuracy is shown in the one row for accuracy, specifically .60.

Upvotes: 4

desertnaut
desertnaut

Reputation: 60321

Your classification report is not missing anything; it is a peculiarity of scikit-learn that it chooses to display the accuracy there, but there is no "precision accuracy" or "recall accuracy". Your actual accuracy is what is shown under the f1-score column; here is an example with toy data from the documentation:

from sklearn.metrics import classification_report
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(classification_report(y_true, y_pred, target_names=target_names))

Result:

              precision    recall  f1-score   support

     class 0       0.50      1.00      0.67         1
     class 1       0.00      0.00      0.00         1
     class 2       1.00      0.67      0.80         3

    accuracy                           0.60         5
   macro avg       0.50      0.56      0.49         5
weighted avg       0.70      0.60      0.61         5

i.e. the accuracy here is 0.6, something that you can directly verify:

from sklearn.metrics import accuracy_score
accuracy_score(y_true, y_pred)
# 0.6

You are right that it's odd, though, and it can certainly be confusing. Not a great design choice...

Upvotes: 2

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