How are feature selection and classification techniques used?
This paper presents a survey on the utilization of feature selection and classification techniques for the diagnosis and prediction of chronic diseases. Adequate selection of features plays a significant role for enhancing accuracy of classification systems.
How are feature selection methods used in disease prediction?
The utilization of feature selection methods is done on clinical databases for the prediction of numerous chronic diseases like diabetes, heart disease, strokes, hypertension, thalassemia etc. Various learning algorithms work efficiently and give more accurate results if the data contains more significant and non-redundant attributes.
How is feature selection used in price prediction?
Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.
How to choose a feature selection method for machine learning?
Numerical Input, Categorical Output This is a classification predictive modeling problem with numerical input variables. This might be the most common example of a classification problem, Again, the most common techniques are correlation based, although in this case, they must take the categorical target into account.
How are the methods of classification and prediction related?
Here is the criteria for comparing the methods of Classification and Prediction − Accuracy − Accuracy of classifier refers to the ability of classifier. It predict the class label correctly and the accuracy of the predictor refers to how well a given predictor can guess the value of predicted attribute for a new data.
How is accuracy of predictor and classification related?
Comparison of Classification and Prediction Methods. Accuracy − Accuracy of classifier refers to the ability of classifier. It predict the class label correctly and the accuracy of the predictor refers to how well a given predictor can guess the value of predicted attribute for a new data.