Course: More Data Mining with Weka

Neural network learning methods invite an analogy to the brain that is seductive but entirely misleading! The simplest form of neural network, called a “Perceptron”, implements a linear decision boundary. …

In this course (and its predecessor, Data Mining with Weka), we’ve been obsessed with classification accuracy – the percentage of correct predictions on test data – as the measure of …

Removing attributes from a dataset before applying a classifier often results in better performance. It’s ironic that removing information can improve performance! Isn’t the whole idea of data mining to …

Even if you don’t do the exercise, you should look at the numbers and note that they support the following general conclusions for the credit-g dataset with a particular cost …

There are two different ways to make a classifier cost-sensitive. One is to create the classifier in the usual way, striving to minimize the number of errors rather than their …

So far we’ve taken the classification rate – computed on a test set, or holdout, or cross-validation – as the measure of a classifier’s success. We’re trying to maximize the …

The attribute selection methods we have examined so far strive to eliminate both irrelevant attributes and redundant ones. A simpler idea is to rank the effectiveness of each individual attribute, …

Attribute selection methods that do not involve a classifier can be faster than the wrapper method. They can use the same kind of searching, but evaluate each subset using a …

Experimenting with a dataset to select attributes and applying a classifier to the result is cheating, if performance is evaluated using cross-validation, because the entire dataset is used to determine …

Fewer attributes often yield better performance! In a laborious manual process, you can start with the full attribute set and remove the best attribute by selectively trying all possibilities, and …

This course so far – like it’s predecessor, Data Mining with Weka – has focused solely on tasks whose aim is to predict the value of a particular attribute called …

We haven’t talked much about rules. We’ve spent a lot of time generating decision trees from datasets – the data mining method you’ve encountered most frequently so far is J48, …

Different clustering algorithms use different metrics for optimization internally, which makes the results hard to evaluate and compare. Weka allows you to visualize clusters, so you can evaluate them by …

With clustering, there’s no “class” attribute: we’re just trying to divide the instances into natural groups or “clusters”. There are different ways of representing clusters. Are they disjoint, or can …

The top 10 rules involve total = high and predict bread-and-cake, supported by 723 transactions. They all have a consequent of “bread and cake” They all indicate a high total …