The decision tree is being experimented on the given heart.csv age sex chest pain type (4 values) resting blood pressure serum cholestoral in mg/dl fasting blood sugar > 120 mg/dl resting electrocardiographic results (values 0,1,2) maximum heart rate achieved exercise induced angina oldpeak = ST depression induced by exercise relative to rest the slope of the peak exercise ST segment number of major vessels (0-3) colored by flourosopy thal: 0 = normal; 1 = fixed defect; 2 = reversable defect
#recall->tp / (tp + fn) #The recall is the measure of our model correctly identifying True Positives. #Thus, for all the customers who actually have heart disease, recall tells us how many we correctly identified as a heart patient. #precision of class 0 = TP of class 0/total number of object #What is the Precision for our model? Yes, it is 0.843 or, when it predicts that a patient has heart disease, it is correct around 84% of the time. #precision of class 1 = TP of class 1/total number of object
#macro average = (precision of class 0 + precision of class 1)/2
#weighted average is precision of all classes merge together #weighted average = (TP of class 0 + TP of class 1)/(total number of class 0 + total number of class 1)
#F1-score is a measure of a model's accuracy on a dataset #a good F1 score means that you have low false positives and low false negatives, #Accuracy is used when the True Positives and True negatives are more important while #F1-score is used when the False Negatives and False Positives are crucial. #Support is the number of actual occurrences of the class in the specified dataset.