Classification

Description:

Classification algorithms are used to identify into which classes observations of data should fall. This problem could be considered part of pattern recognition in that we use training data (historical information) to recognize patterns to predict where new data should be categorized.

Common Use Cases:

• Fraudulent activity detection

• Loan default prediction

• Spam vs. ham

• Customer segmentation

• Benign vs. malignant tumor classification

• and many more...

Classification Algorithms included in MLlib:

• Logistic regression (both binomial and multiclass)

• Decision trees

• Random forests

• Gradient-boosted trees

• Multilayer perceptron

• Linear Support Vector Machine

• One-vs-Rest classifier (a.k.a. One-vs-All)

• Naïve Bayes