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You can use Classification Learner to automatically train a selection of different classification models on your data. Use automated training to quickly try a selection of model types, then explore promising models interactively. To get started, try these options first:

Get Started Classifier Buttons | Description |
---|---|

All Quick-To-Train | Try this first. The app will train all the model types available for your data set that are typically fast to fit. |

All Linear | Try this if you expect linear boundaries between the classes in your data. This option fits only Linear SVM and Linear Discriminant. |

All | Use this to train all available model types. Trains every type regardless of any prior trained models. Can be time-consuming. |

See Automated Classifier Training.

If you want to explore classifiers one at a time, or you already
know what classifier type you want, you can select individual models
or train a group of the same type. To see all available classifier
options, on the **Classification Learner** tab, click
the arrow on the far right of the **Model Type** section
to expand the list of classifiers. The options in the **Model
Type** gallery are preset starting points with different
settings, suitable for a range of different classification problems.

For help choosing the best classifier type for your problem, see the table showing typical characteristics of different supervised learning algorithms. Use the table as a guide for your final choice of algorithms. Decide on the tradeoff you want in speed, memory usage, flexibility, and interpretability. The best classifier type depends on your data.

To avoid overfitting, look for a model of lower flexibility that provides sufficient accuracy. For example, look for simple models such as decision trees and discriminants that are fast and easy to interpret. If the models are not accurate enough predicting the response, choose other classifiers with higher flexibility, such as ensembles. To control flexibility, see the details for each classifier type.

**Characteristics of Classifier Types**

Classifier | Prediction Speed | Memory Usage | Interpretability |
---|---|---|---|

Fast | Small | Easy | |

Fast | Small for linear, large for quadratic | Easy | |

Fast | Medium | Easy | |

## NoteSupports C code generation for prediction. | 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. |

Slow for cubic. Medium for others. | Medium | Hard | |

Fast to medium depending on choice of algorithm | Low to high depending on choice of algorithm | Hard |

The tables on this page describe general characteristics of speed and memory usage for all the preset classifiers. The classifiers were tested with various data sets (up to 7000 observations, 80 predictors, and 50 classes), and the results define the following groups:

Speed

Fast 0.01 second

Medium 1 second

Slow 100 seconds

Memory

Small 1MB

Medium 4MB

Large 100MB

These tables provide a general guide. Your results depend on your data and the speed of your machine.

To read a description of each classifier in Classification Learner, switch to the details view.

After you choose a classifier type (e.g., decision trees), try
training using each of the classifiers. The options in the **Model
Type** gallery are starting points with different settings.
Try them all to see which option produces the best model with your
data.

For workflow instructions, see Train Classification Models in Classification Learner App.

In Classification Learner, the **Model Type** gallery
only shows classifier types that support your selected data.

Classifier | All predictors numeric | All predictors categorical | Some categorical, some numeric |
---|---|---|---|

Decision Trees | Yes | Yes | Yes |

Discriminant Analysis | Yes | No | No |

Logistic Regression | Yes | Yes | Yes |

SVM | Yes | Yes | Yes |

Nearest Neighbor | Euclidean distance only | Hamming distance only | No |

Ensembles | Yes | Yes, except Subspace Discriminant | Yes, except any Subspace |

Decision trees are easy to interpret, fast for fitting and prediction,
and low on memory usage, but they can have low predictive accuracy.
Try to grow simpler trees to prevent overfitting. Control the depth
with the **Maximum number of splits** setting.

Model flexibility increases with the **Maximum number
of splits** setting.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Model Flexibility |
---|---|---|---|---|

Coarse Tree | Fast | Small | Easy | Low. Few leaves to make coarse distinctions between classes (maximum number of splits is 4). |

Medium Tree | Fast | Small | Easy | Medium Medium number of leaves for finer distinctions between classes (maximum number of splits is 20). |

Fine Tree | Fast | Small | Easy | High Many leaves to make many fine distinctions between classes (maximum number of splits is 100). |

In the **Model Type** gallery click All Trees
to try each of the decision tree options . Train them all to see which
settings produce the best model with your data. Select the best model
in the History list. To try to improve your model, try feature selection,
and then try changing some advanced options.

You train classification trees to predict responses to data. To predict a response, follow the decisions in the tree from the root (beginning) node down to a leaf node. The leaf node contains the response. Statistics and Machine Learning Toolbox™ trees are binary. Each step in a prediction involves checking the value of one predictor (variable). For example, here is a simple classification tree:

This tree predicts classifications based on two predictors, `x1`

and `x2`

.
To predict, start at the top node. At each decision, check the values
of the predictors to decide which branch to follow. When the branches
reach a leaf node, the data is classified either as type `0`

or `1`

.

You can visualize your decision tree model by exporting the model from the app, and then entering:

view(trainedModel.ClassificationTree,'Mode','graph')

`fisheriris`

data.
For an example, see Train Decision Trees Using Classification Learner App.

Classification trees in Classification Learner use the `fitctree`

function.
You can set these options:

**Maximum number of splits**Specify the maximum number of splits or branch points to control the depth of your tree. When you grow a decision tree, consider its simplicity and predictive power. To change the number of splits, click the buttons or enter a positive integer value in the

**Maximum number of splits**box.A fine tree with many leaves is usually highly accurate on the training data. However, the tree might not show comparable accuracy on an independent test set. A leafy tree tends to overtrain, and its validation accuracy is often far lower than its training (or resubstitution) accuracy.

In contrast, a coarse tree does not attain high training accuracy. But a coarse tree can be more robust in that its training accuracy can approach that of a representative test set. Also, a coarse tree is easy to interpret.

**Split criterion**Specify the split criterion measure for deciding when to split nodes. Try each of the three settings to see if they improve the model with your data.

Split criterion options are

`Gini's diversity index`

,`Twoing rule`

, or`Maximum deviance reduction`

(also known as cross entropy).The classification tree tries to optimize to pure nodes containing only one class. Gini's diversity index (the default) and the deviance criterion measure node impurity. The twoing rule is a different measure for deciding how to split a node, where maximizing the twoing rule expression increases node purity.

For details of these split criteria, see

`ClassificationTree`

Definitions.**Surrogate decision splits**— Only for missing data.Specify surrogate use for decision splits. If you have data with missing values, use surrogate splits to improve the accuracy of predictions.

When you set

**Surrogate decision splits**to`On`

, the classification tree finds at most 10 surrogate splits at each branch node. To change the number, click the buttons or enter a positive integer value in the**Maximum surrogates per node**box.When you set

**Surrogate decision splits**to`Find All`

, the classification tree finds all surrogate splits at each branch node. The`Find All`

setting can use considerable time and memory.

Discriminant analysis is a popular first classification algorithm to try because it is fast, accurate and easy to interpret. Discriminant analysis is good for wide datasets.

Discriminant analysis assumes that different classes generate data based on different Gaussian distributions. To train a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Model Flexibility |
---|---|---|---|---|

Linear Discriminant | Fast | Small | Easy | Low. Creates linear boundaries between classes. |

Quadratic Discriminant | Fast | Large | Easy | Low. Creates nonlinear boundaries between classes (ellipse, parabola or hyperbola). |

Discriminant analysis in Classification Learner uses the `fitcdiscr`

function.
For both linear and quadratic discriminants, you can change the **Covariance
structure** option. If you have predictors with zero variance
or if any of the covariance matrices of your predictors are singular,
training can fail using the default, `Full`

covariance
structure. If training fails, select the `Diagonal`

covariance
structure instead.

If you have 2 classes, logistic regression is a popular simple classification algorithm to try because it is easy to interpret. The classifier models the class probabilities as a function of the linear combination of predictors.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Model Flexibility |
---|---|---|---|---|

Logistic Regression | Fast | Medium | Easy | Low. You cannot change any parameters to control model flexibility. |

Logistic regression in Classification Learner uses the `fitglm`

function.
You cannot set any options for this classifier in the app.

In Classification Learner, you can train SVMs when your data has two or more classes.

After you train an SVM model, you can generate C code for prediction.
Requires MATLAB^{®}
Coder™. See Generate C Code for Prediction.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Model Flexibility |
---|---|---|---|---|

Linear SVM | Binary: Fast Multiclass: Medium | Medium | Easy | Low Makes a simple linear separation between classes. |

Quadratic SVM | Binary: Fast Multiclass: Slow | Binary: Medium Multiclass: Large | Hard | Medium |

Cubic SVM | Binary: Fast Multiclass: Slow | Binary: Medium Multiclass: Large | Hard | Medium |

Fine Gaussian SVM | Binary: Fast Multiclass: Slow | Binary: Medium Multiclass: Large | Hard | High — decreases with kernel scale setting. Makes
finely detailed distinctions between classes, with kernel scale set
to |

Medium Gaussian SVM | Binary: Fast Multiclass: Slow | Binary: Medium Multiclass: Large | Hard | Medium Medium distinctions, with kernel scale set to |

Coarse Gaussian SVM | Binary: Fast Multiclass: Slow | Binary: Medium Multiclass: Large | Hard | Low Makes coarse distinctions between classes, with kernel
scale set to |

Try training each of the support vector machine options in the **Model
Type** gallery. Train them all to see which settings produce
the best model with your data. Select the best model in the History
list. To try to improve your model, try feature selection, and then
try changing some advanced options.

An SVM classifies data by finding the best hyperplane that separates data points of one class from those of the other class. The best hyperplane for an SVM means the one with the largest margin between the two classes. Margin means the maximal width of the slab parallel to the hyperplane that has no interior data points.

The *support vectors* are the data points
that are closest to the separating hyperplane; these points are on
the boundary of the slab. The following figure illustrates these definitions,
with + indicating data points of type 1, and – indicating data
points of type –1.

SVMs can also use a soft margin, meaning a hyperplane that separates many, but not all data points.

For an example, see Train Support Vector Machines Using Classification Learner App.

If you have exactly two classes, Classification Learner uses
the `fitcsvm`

function to train the classifier.
If you have more than two classes, the app uses the `fitcecoc`

function
to reduce the multiclass classification problem to a set of binary
classification subproblems, with one SVM learner for each subproblem.
To examine the code for the binary and multiclass classifier types,
you can generate code from your trained classifiers in the app.

You can set these options in the app:

**Kernel function**Specify the Kernel function to compute the classifier.

Linear kernel, easiest to interpret

Gaussian or Radial Basis Function (RBF) kernel

Quadratic

Cubic

**Box constraint level**Specify the box constraint to keep the allowable values of the Lagrange multipliers in a box, a bounded region.

To tune your SVM classifier, try increasing the box constraint level. Click the buttons or enter a positive scalar value in the

**Box constraint level**box. Increasing the box constraint level can decrease the number of support vectors, but also can increase training time.The Box Constraint parameter is the soft-margin penalty known as C in the primal equations, and is a hard “box” constraint in the dual equations.

**Kernel scale mode**Specify manual kernel scaling if desired.

When you set

**Kernel scale mode**to`Auto`

, then the software uses a heuristic procedure to select the scale value. The heuristic procedure uses subsampling. Therefore, to reproduce results, set a random number seed using`rng`

before training the classifier.When you set

**Kernel scale mode**to`Manual`

, you can specify a value. Click the buttons or enter a positive scalar value in the**Manual kernel scale**box. The software divides all elements of the predictor matrix by the value of the kernel scale. Then, the software applies the appropriate kernel norm to compute the Gram matrix.### Tip

Model flexibility decreases with the kernel scale setting.

**Multiclass method**Only for data with 3 or more classes. This method reduces the multiclass classification problem to a set of binary classification subproblems, with one SVM learner for each subproblem.

`One-vs-One`

trains one learner for each pair of classes. It learns to distinguish one class from the other.`One-vs-All`

trains one learner for each class. It learns to distinguish one class from all others.**Standardize data**Specify whether to scale each coordinate distance. If predictors have widely different scales, standardizing can improve the fit.

Nearest neighbor classifiers typically have good predictive accuracy in low dimensions, but might not in high dimensions. They have high memory usage, and are not easy to interpret.

Model flexibility decreases with the **Number of neighbors** setting.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Model Flexibility |
---|---|---|---|---|

Fine KNN | Medium | Medium | Hard | Finely detailed distinctions between classes. The number of neighbors is set to 1. |

Medium KNN | Medium | Medium | Hard | Medium distinctions between classes. The number of neighbors is set to 10. |

Coarse KNN | Medium | Medium | Hard | Coarse distinctions between classes. The number of neighbors is set to 100. |

Cosine KNN | Medium | Medium | Hard | Medium distinctions between classes, using a Cosine distance metric. The number of neighbors is set to 10. |

Cubic KNN | Slow | Medium | Hard | Medium distinctions between classes, using a cubic distance metric. The number of neighbors is set to 10. |

Weighted KNN | Medium | Medium | Hard | Medium distinctions between classes, using a distance weight. The number of neighbors is set to 10. |

Try training each of the nearest neighbor options in the **Model
Type** gallery. Train them all to see which settings produce
the best model with your data. Select the best model in the History
list. To try to improve your model, try feature selection, and then
(optionally) try changing some advanced options.

What is *k*-Nearest Neighbor classification?
Categorizing query points based on their distance to points (or neighbors)
in a training dataset can be a simple yet effective way of classifying
new points. You can use various metrics to determine the distance.
Given a set *X* of *n* points and
a distance function, *k*-nearest neighbor (*k*NN)
search lets you find the *k* closest points in *X* to
a query point or set of points. *k*NN-based algorithms
are widely used as benchmark machine learning rules.

For an example, see Train Nearest Neighbor Classifiers Using Classification Learner App.

Nearest Neighbor classifiers in Classification Learner use the `fitcknn`

function.
You can set these options:

**Number of neighbors**Specify the number of nearest neighbors to find for classifying each point when predicting. Specify a fine (low number) or coarse classifier (high number) by changing the number of neighbors. For example, a fine KNN uses one neighbor, and a coarse KNN uses 100. Many neighbors can be time consuming to fit.

**Distance metric**You can use various metrics to determine the distance to points. For definitions, see the class

`ClassificationKNN`

.**Distance weight**Specify the distance weighting function. You can choose

`Equal`

(no weights),`Inverse`

(weight is 1/distance), or`Squared Inverse`

(weight is 1/distance^{2}).**Standardize data**Specify whether to scale each coordinate distance. If predictors have widely different scales, standardizing can improve the fit.

Ensemble classifiers meld results from many weak learners into one high-quality ensemble model. Qualities depend on the choice of algorithm.

Model flexibility increases with the **Number of learners** setting.

All ensemble classifiers tend to be slow to fit because they often need many learners.

Classifier Type | Prediction Speed | Memory Usage | Interpretability | Ensemble Method | Model Flexibility |
---|---|---|---|---|---|

Boosted Trees | Fast | Low | Hard | `AdaBoost` , with ```
Decision
Tree
``` learners | Medium to high — increases with ## TipBoosted trees can usually do better than bagged, but might require parameter tuning and more learners |

Bagged Trees | Medium | High | Hard | Random forest`Bag` ,
with `Decision Tree` learners | High — increases with ## TipTry this classifier first. |

Subspace Discriminant | Medium | Low | Hard | `Subspace` , with `Discriminant` learners | Medium — increases with Good for many predictors |

Subspace KNN | Medium | Medium | Hard | `Subspace` , with ```
Nearest
Neighbor
``` learners | Medium — increases with Good for many predictors |

RUSBoost Trees | Fast | Low | Hard | `RUSBoost` , with ```
Decision
Tree
``` learners | Medium — increases with Good for skewed data (with many more observations of 1 class) |

GentleBoost or LogitBoost — not available in the Model
Type gallery. If you have 2 class data, select manually. | Fast | Low | Hard | `GentleBoost` or `LogitBoost` ,
with `Decision Tree` learnersChoose ```
Boosted
Trees
``` and change to `GentleBoost` method. | Medium — increases with For binary classification only |

Bagged trees use Breiman's `'random forest'`

algorithm.
For reference, see Breiman, L. Random Forests. Machine Learning 45,
pp. 5–32, 2001.

Tips

Try bagged trees first. Boosted trees can usually do better but might require searching many parameter values, which is time-consuming.

Try training each of the ensemble classifier options in the

**Model Type**gallery. Train them all to see which settings produce the best model with your data. Select the best model in the History list. To try to improve your model, try feature selection, PCA, and then (optionally) try changing some advanced options.For boosting ensemble methods, you can get fine detail with either deeper trees or larger numbers of shallow trees. As with single tree classifiers, deep trees can cause overfitting. You need to experiment to choose the best tree depth for the trees in the ensemble, in order to trade-off data fit with tree complexity. Use the

**Number of learners**and**Maximum number of splits**settings.

For an example, see Train Ensemble Classifiers Using Classification Learner App.

Ensemble classifiers in Classification Learner use the `fitcensemble`

function.
You can set these options:

For help choosing

**Ensemble method**and**Learner type**, see the Ensemble table. Try the presets first.**Maximum number of splits**For boosting ensemble methods, specify the maximum number of splits or branch points to control the depth of your tree learners. Many branches tend to overfit, and simpler trees can be more robust and easy to interpret. Experiment to choose the best tree depth for the trees in the ensemble.

**Number of learners**Try changing the number of learners to see if you can improve the model. Many learners can produce high accuracy, but can be time consuming to fit. Start with a few dozen learners, and then inspect the performance. An ensemble with good predictive power can need a few hundred learners.

**Learning rate**Specify the learning rate for shrinkage. If you set the learning rate to less than 1, the ensemble requires more learning iterations but often achieves better accuracy. 0.1 is a popular choice.

**Subspace dimension**For subspace ensembles, specify the number of predictors to sample in each learner. The app chooses a random subset of the predictors for each learner. The subsets chosen by different learners are independent.

For next steps training models, see Train Classification Models in Classification Learner App.

- Train Classification Models in Classification Learner App
- Select Data and Validation for Classification Problem
- Feature Selection and Feature Transformation Using Classification Learner App
- Assess Classifier Performance in Classification Learner
- Export Classification Model to Predict New Data
- Train Decision Trees Using Classification Learner App