Scala and Spark for Big Data Analytics

Performance metrics

While there are many different types of classification algorithms, evaluation metrics more or less shares similar principles. In a supervised classification problem, there exists a true output and a model-generated predicted output for each data point. For this reason, the results for each data point can be assigned to one of four categories:

• True positive (TP): Label is positive and prediction is also positive.
• True negative (TN): Label is negative and prediction is also negative.
• False positive (FP): Label is negative but prediction is positive.
• False negative (FN): Label is positive but prediction is negative.

Now, to get a clearer idea about these parameters, refer to the following figure:

Figure 23: Prediction ...