The aim of supervised, machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive algorithms identify patterns in data, a computer "learns" from the observations. When exposed to more observations, the computer improves its predictive performance.
Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.
For example, suppose you want to predict whether someone will have a heart attack within a year. You have a set of data on previous patients, including age, weight, height, blood pressure, etc. You know whether the previous patients had heart attacks within a year of their measurements. So, the problem is combining all the existing data into a model that can predict whether a new person will have a heart attack within a year.
You can think of the entire set of input data as a heterogeneous matrix. Rows of the matrix are called observations, examples, or instances, and each contain a set of measurements for a subject (patients in the example). Columns of the matrix are called predictors, attributes, or features, and each are variables representing a measurement taken on every subject (age, weight, height, etc. in the example). You can think of the response data as a column vector where each row contains the output of the corresponding observation in the input data (whether the patient had a heart attack). To fit or train a supervised learning model, choose an appropriate algorithm, and then pass the input and response data to it.
Supervised learning splits into two broad categories: classification and regression.
In classification, the goal is to assign a class (or
label) from a finite set of classes to an observation. That is,
responses are categorical variables. Applications include spam filters, advertisement
recommendation systems, and image and speech recognition. Predicting whether a patient
will have a heart attack within a year is a classification problem, and the possible
algorithms usually apply to nominal response values. However, some algorithms can
accommodate ordinal classes (see
In regression, the goal is to predict a continuous measurement for an observation. That is, the responses variables are real numbers. Applications include forecasting stock prices, energy consumption, or disease incidence.
Statistics and Machine Learning Toolbox™ supervised learning functionalities comprise a stream-lined, object framework. You can efficiently train a variety of algorithms, combine models into an ensemble, assess model performances, cross-validate, and predict responses for new data.
While there are many Statistics and Machine Learning Toolbox™ algorithms for supervised learning, most use the same basic workflow for obtaining a predictor model. (Detailed instruction on the steps for ensemble learning is in Framework for Ensemble Learning.) The steps for supervised learning are:
All supervised learning methods start with an input data matrix, usually called
X here. Each row of
X represents one observation.
Each column of
X represents one variable, or predictor. Represent
missing entries with
NaN values in
X. Statistics and Machine
Learning Toolbox supervised learning algorithms can handle
either by ignoring them or by ignoring any row with a
You can use various data types for response data
Y. Each element in
Y represents the response to the corresponding row of
X. Observations with missing
Y data are
Y must be a numeric vector with the same number
of elements as the number of rows of
Y can be any of these data types. This
table also contains the method of including missing entries.
|Data Type||Missing Entry|
|Character array||Row of spaces|
|Cell array of character vectors|
|Logical vector||(Cannot represent)|
There are tradeoffs between several characteristics of algorithms, such as:
Speed of training
Predictive accuracy on new data
Transparency or interpretability, meaning how easily you can understand the reasons an algorithm makes its predictions
The fitting function you use depends on the algorithm you choose.
|Discriminant Analysis (classification)|
|k-Nearest Neighbors (classification)|
|Naive Bayes (classification)|
|Support Vector Machines (SVM) for classification|
|SVM for regression|
|Multiclass models for SVM or other classifiers|
|Classification or Regression Tree Ensembles (e.g., Random Forests ) in Parallel|
For a comparison of these algorithms, see Characteristics of Classification Algorithms.
The three main methods to examine the accuracy of the resulting fitted model are:
Examine the resubstitution error. For examples, see:
Examine the cross-validation error. For examples, see:
Examine the out-of-bag error for bagged decision trees. For examples, see:
After validating the model, you might want to change it for better accuracy, better speed, or to use less memory.
Change fitting parameters to try to get a more accurate model. For examples, see:
Change fitting parameters to try to get a smaller model. This sometimes gives a model with more accuracy. For examples, see:
Try a different algorithm. For applicable choices, see:
When satisfied with a model of some types, you can trim it using the appropriate
compact function (
compact for classification trees,
compact for regression trees,
compact for discriminant analysis,
compact for naive Bayes,
compact for SVM,
compact for ECOC models,
compact for classification ensembles, and
compact for regression ensembles).
removes training data and other properties not required for prediction, e.g., pruning
information for decision trees, from the model to reduce memory consumption. Because
kNN classification models require all of the training data to predict
labels, you cannot reduce the size of a
To predict classification or regression response for most fitted models, use the
Ypredicted = predict(obj,Xnew)
obj is the fitted model or fitted compact model.
Xnew is the new input data.
Ypredicted is the predicted response, either classification or
This table shows typical characteristics of the various supervised learning algorithms. The characteristics in any particular case can vary from the listed ones. Use the table as a guide for your initial choice of algorithms. Decide on the tradeoff you want in speed, memory usage, flexibility, and interpretability.
Try a decision tree or discriminant first, because these classifiers are fast and easy to interpret. If the models are not accurate enough predicting the response, try other classifiers with higher flexibility.
To control flexibility, see the details for each classifier type. To avoid overfitting, look for a model of lower flexibility that provides sufficient accuracy.
|Classifier||Multiclass Support||Categorical Predictor Support||Prediction Speed||Memory Usage||Interpretability|
|Decision Trees — ||Yes||Yes||Fast||Small||Easy|
— ||Yes||No||Fast||Small for linear, large for quadratic||Easy|
|SVM — ||No.|
Combine multiple binary SVM classifiers using
|Yes||Medium for linear.|
Slow for others.
|Medium for linear.|
All others: medium for multiclass, large for binary.
|Easy for linear SVM.|
Hard for all other kernel types.
|Naive Bayes —
||Yes||Yes||Medium for simple distributions.|
Slow for kernel distributions or high-dimensional data
|Small for simple distributions.|
Medium for kernel distributions or high-dimensional data
|Nearest neighbor —
||Yes||Yes||Slow for cubic.|
Medium for others.
||Yes||Yes||Fast to medium depending on choice of algorithm||Low to high depending on choice of algorithm.||Hard|
The results in this table are based on an analysis of many data sets. The data sets in the study have up to 7000 observations, 80 predictors, and 50 classes. This list defines the terms in the table.
Fast — 0.01 second
Medium — 1 second
Slow — 100 seconds
Small — 1MB
Medium — 4MB
Large — 100MB
The table provides a general guide. Your results depend on your data and the speed of your machine.
This table describes the data-type support of predictors for each classifier.
|Classifier||All predictors numeric||All predictors categorical||Some categorical, some numeric|
|Nearest Neighbor||Euclidean distance only||Hamming distance only||No|
|Ensembles||Yes||Yes, except subspace ensembles of discriminant analysis classifiers||Yes, except subspace ensembles|
 Breiman, L. Random Forests. Machine Learning 45, 2001, pp. 5–32.