![Matrix
• True Negative [TN]: Model has given prediction No, and the real or
actual value was also No.
• True Positive [TP]: The model has predicted yes, and the actual value
was also true.
• False Negative [FN]: The model has predicted no, but the actual value
was Yes, it is also called as Type-II error.
• False Positive [FP]: The model has predicted Yes, but the actual value
was No. It is also called a Type-I error.](https://image.slidesharecdn.com/confusionmatrix-230923041700-cb7d8ca1/85/CONFUSION-MATRIX-ppt-3-320.jpg)
CONFUSION MATRIX.ppt
- 2. MEANING • A tool for evaluating the performance of a classification model for a given set of test data • Provides a detailed breakdown of how well a model is performing in terms of classifying instances into different classes • For 2 prediction classes of classifiers, the matrix is of 2*2 table • For 3 classes, it is 3*3 table • The matrix is divided into two dimensions, that are predicted values and actual values. • Predicted values are values, which are predicted by the model, and actual values are true values for the given observations.
- 3. Matrix • True Negative [TN]: Model has given prediction No, and the real or actual value was also No. • True Positive [TP]: The model has predicted yes, and the actual value was also true. • False Negative [FN]: The model has predicted no, but the actual value was Yes, it is also called as Type-II error. • False Positive [FP]: The model has predicted Yes, but the actual value was No. It is also called a Type-I error.
- 4. Need for Confusion Matrix • Evaluates the performance of the classification models, making predictions on test data, and tells how good classification model is. • Tells the error made by the classifiers but also the type of errors such as either type-I or type-II error. • Helpful to calculate the parameters for classification model, such as accuracy, precision, etc.
- 5. Sensitivity and Specificity • Sensitivity tells us what proportion of the positive class got correctly classified. • A simple example would be determining what proportion of the actual sick people were correctly detected by the model. • False Negative Rate (FNR) tells us what proportion of the positive class got incorrectly classified by the classifier. • A higher TPR and lower FNR are desirable since we want to classify the positive class correctly. • Specificity tells us what proportion of the negative class got correctly classified.
- 6. Calculation using Confusion Matrix • Classification Accuracy: Defines how often the model predicts the correct output • Misclassification rate: Also termed as Error rate, and it defines how often the model gives the wrong predictions. • Precision: It can be defined as the number of correct outputs provided by the model or out of all positive classes that have predicted correctly by the model. • Recall: It is defined as the out of total positive classes, how our model predicted correctly. • F-measure: Score helps us to evaluate the recall and precision at the same time.