Clustering groups data entities based on their feature values. Each data entity is described as a vector of one or more numeric features (x), which can be used to plot the entity as a specific point.
K-Means clustering is a technique in which the algorithm groups the data entities into a specified number of clusters (k). To begin, the algorithm selects k random centroid points, and assigns each data entity to a cluster based on the shortest distance to a centroid. Each centroid is then moved to the central point (i.e. the mean) of its cluster, and the distances between the data entities and the new centroids are evaluated, with entities reassigned to another cluster if it is closer. This process continues until every data entity is closer to the mean centroid of its cluster than to the centroid of any other cluster. The following image shows the result of K=Means clustering with a k value of 3:
Hierarchical Agglomerative Clustering is an alternative clustering technique in which the point representing each data entity begins as its own cluster. Each cluster is then merged with the cluster closest to it, and this merging process continues iteratively. The following image illustrates the process of Hierarchical Agglomerative Clustering.
The distance metric used to determine how “close” points are to one another is an important aspect of clustering. The simplest way to conceptualize the entities is as points in Euclidean space (multidimensional coordinates), and measure the simple distance between the points; but other distance metrics can also be used.
For a classification function to work accurately, when operating on the data in the training and test datasets, the number of times that the sign of f(x) does not equal y must be minimized. In other words, for a single entity, if y is positive, f(x) should be positive; and if y is negative, f(x) should be negative. Formally, we need to minimize cases where y ≠ sign(f(x)) to let the classification function to work accurately.
Because same-signed numbers when multiplied together always produce a positive, and numbers of different signs multiplied together always produce a negative, we can simplify our goal to minimize cases where yf(x) < 0; or for the whole data set ∑iyif(xi) < 0. This general approach is known as a loss function.
As with regression algorithms, some classification algorithms add a regularization term to avoid over-fitting so that the function achieves a balance of accuracy and simplicity (Occam’s Razor again).
Each classification algorithm (for example AdaBoost, Support Vector Machines, and Logistic Regression) uses a specific loss function implementation, and it’s this that distinguishes classification algorithms from one another.
Decision Trees and Multi-Class Classification
Decision Trees are classification algorithms that define a sequence of branches. At each branch intersection, the feature value (x) is compared to a specific function, and the result determines which branch the algorithm follows. All branches eventually lead to a predicted value (-1 or +1). Most decision trees algorithms have been around for a while, and many produce low accuracy. However, boosted decision trees (AdaBoost applied to a decision tree) can be very effective.
You can use a “one vs. all” technique to extend binary classification (which predicts a Boolean value) so that it can be used in multi-class classification. This approach involves applying multiple binary classifications (for example, “is this a chair?”, “is this a bird?”, and so on) and reviewing the results produced by f(x) for each test. Since f(x) produces a numeric result, the predicted value is a measure of confidence in the prediction (so for example, a high positive result for “is this a chair?” combined with a low positive result for “is this a bird?” and a high negative result for “is this an elephant?” indicates a high degree of confidence that the object is more likely to be a chair than a bird, and very unlikely to be an elephant.)
When the training data is imbalanced (so a high proportion of the data has the same True/False value for y) , the accuracy of the classification algorithm can be compromised. To overcome this problem, you can “over-sample” or “weight” data with the minority y value to balance the algorithm.
ROC – Receiver Operator Characteristic
The quality of a classification model can be assessed by plotting the True Positive Rate (predicted positives / actual positives) against the False Positive Rate (predicted negatives / actual negatives) for various parameter values on a chart to create a receiver operator characteristic (ROC) curve. The quality of the model is reflected in the area under the curve (AOC). The larger this area is than the area under a straight diagonal line (representing a 50% accuracy rate that can be achieved purely by guessing), the better the model; as shown below: