CONFUSION MATRIX.ppt

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A confusion matrix is a tool used to evaluate classification models on test data. It provides a breakdown of correct and incorrect predictions made by the model compared to actual classifications. The matrix has rows for predicted classifications and columns for actual classifications. It allows calculating important metrics like accuracy, precision, recall, and F1 score to assess model performance.

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.

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.

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.

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.

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.

A confusion matrix is a tool used to evaluate the performance of a supervised machine learning model for classification problems. It allows visualization of correct and incorrect predictions compared to the actual classifications in a test dataset. The confusion matrix shows the true positives, false positives, true negatives, and false negatives. This helps determine the accuracy, precision, recall, F1 score and area under the curve (AUC) of the model, which are more comprehensive metrics for evaluation than accuracy alone.

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CONFUSION MATRIX.ppt

1. CONFUSION MATRIX

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.

CONFUSION MATRIX.ppt

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