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1. Classification groups data into .
2. In logistic regression, 1 means the event and 0 means that the event .
3. In Multiclass Classification, data samples are classified as belonging to one of the
classes.
4. A classification model tries to map the input variable function to discrete output variables.
(State True or False)
5. Name a binary classification algorithm other than Logistic regression.
Confusion Matrix—Evaluating a Classification Model
In the field of machine learning, a confusion matrix (NxN matrix) is used to validate the performance of a classification
model i.e. how good are the classifier’s predictions, where N is the number of target classes. The confusion matrix
compares the actual target values with those predicted by the classifier. This tells us how well the classification model is
performing and what kind of error it is making.
For a binary classification problem, we would have a 2x2 matrix (N = 2 classes) which looks as follows:
Reality
The Confusion
Matrix
Yes No
Yes True Positive (TP) False Positive (FP)
Prediction
No False Negative (FN) True Negative (TN)
(The rows denote the predicted values given by the AI classifier model and the columns denote the actual values of
the target variable. Let us understand the remaining terms.
True Positive (TP)
• The predicted value tallied with the actual value.
• The actual value was positive and the classification model also predicted positive.
• As the values match, there is no error.
True Negative (TN)
• The predicted value tallied with the actual value.
• The actual value was negative and the classification model also predicted negative.
• As the values match, there is no error.
False Positive (FP)
• The predicted value doesn’t tally with the actual value.
Classification & Clustering 303

