What is the classification accuracy in Weka?

Top results are in the order of 98% accuracy. Start the Weka Explorer: Open the Weka GUI Chooser. Click the “Explorer” button to open the Weka Explorer.

What is the accuracy of the classifier?

Classification accuracy is a metric that summarizes the performance of a classification model as the number of correct predictions divided by the total number of predictions. It is easy to calculate and intuitive to understand, making it the most common metric used for evaluating classifier models.

How do you evaluate the accuracy of a classifier in data mining?

You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier. It’s that simple.

What are classifiers in Weka?

A classifier identifies an instance’s “class”, based on a training set. Weka makes it very easy to build classifiers, as Ian Witten demonstrates. Weka has bayes classifiers, functions classifiers, lazy classifiers, meta classifiers, and so on. We’re going to use a tree classifier. J48 is a tree classifier.

What is incorrectly classified instances in Weka?

Similarly, incorrectly classified instances means the sum of FP and FN. The total number of correctly instances divided by total number of instances gives the accuracy. In weka, % of correctly classified instances give the accuracy of the model. Cheers!

What is a good model accuracy?

What Is the Best Score? If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound.

What is a good prediction accuracy?

If you devide that range equally the range between 100-87.5% would mean very good, 87.5-75% would mean good, 75-62.5% would mean satisfactory, and 62.5-50% bad. Actually, I consider values between 100-95% as very good, 95%-85% as good, 85%-70% as satisfactory, 70-50% as “needs to be improved”.

What is classifier accuracy in data mining?

1. Accuracy. The accuracy of a classifier is given as the percentage of total correct predictions divided by the total number of instances. Mathematically, If the accuracy of the classifier is considered acceptable, the classifier can be used to classify future data tuples for which the class label is not known.

How do you evaluate a classifier?

Classifiers are commonly evaluated using either a numeric metric, such as accuracy, or a graphical representation of performance, such as a receiver operating characteristic (ROC) curve. We will examine some common classifier metrics and discuss the pitfalls of relying on a single metric.

How does a classifier work?

A classifier utilizes some training data to understand how given input variables relate to the class. When the classifier is trained accurately, it can be used to detect an unknown email. Classification belongs to the category of supervised learning where the targets also provided with the input data.

How to start the classification process in Weka?

Click on the Start button to start the classification process. After a while, the classification results would be presented on your screen as shown here − Let us examine the output shown on the right hand side of the screen.

How do you split data between training and testing in Weka?

In the percentage split, you will split the data between training and testing using the set split percentage. Next, you will select the classifier. Click on the Choose button and select the following classifier − This is shown in the screenshot below − Click on the Start button to start the classification process.

How to choose a J48 classifier in Weka?

Next, we select the “Classify” tab and click the “Choose” button to select the J48 classifier, as depicted in Figures 21-a and 21-b. Note that J48 (implementation of C4.5 algorithm) does not require discretization of numeric attributes, in contrast to the ID3 algorithm from which C4.5 has evolved.

How big is a tree in Weka classifier?

It says the size of the tree is 6. You will very shortly see the visual representation of the tree. In the Summary, it says that the correctly classified instances as 2 and the incorrectly classified instances as 3, It also says that the Relative absolute error is 110%.