whether you're an expert or just getting
started with machine learning
you often explore different types of
models and repeatedly tune and test
those models
the classification learner app in the
statistics and machine learning toolbox
lets you train classification models
using supervised machine learning
without writing any code
classification learner lets you perform
common machine learning tasks
such as importing pre-processed data
exploring your data
quickly training multiple models
assessing model performance
and the iterative steps required to tune
model performance
including optimizing hyper parameters
misclassification cost
and feature selection you can find the
classification learner in the apps
gallery
or by typing classification learner on
the matlab command line
classification learner lets you import
data from matrices or tables
the app can automatically identify your
predictors and response variables
based on your data type the next step is
to choose the validation scheme
used to measure model accuracy
once the app has loaded the data you can
choose from several classification
algorithms
including decision trees logistic
regression
support vector machines and boosted
trees
if you're not sure which to choose the
pop-up tool tip
gives you a brief description of each
classifier pairwise scatter plots let
you explore your data for important
predictors
outliers and visual patterns or trends
training a new model is easy first in
the model gallery
choose one of the classifier presets or
the train all option
next click on train the current model
pane displays useful information
about your model such as the classifier
type
presets selected features and the status
of the model
for each trained model you can see its
accuracy on the history panel
and inspect its misclassifications in
the scatterplot
look at the confusion matrix and look at
roc curves available in the plot section
of the tool strip
the confusion matrix lets you assess how
the currently selected classifier
performed in each class
good classifiers have a dominantly
diagonal confusion matrix
since all the predictor labels match the
actual labels
off-diagonal numbers indicate confusions
between classes
you can force the classifier to produce
specific misclassifications
by placing a higher cost on them during
training
to do so select misclassification costs
from the tool strip
specify uneven misclassification cost
and then retrain the model to see
whether it helped reduce the undesired
misclassifications
based on your assessment of how a model
may be improved further
you can use the advanced options to
change classifier settings
or remove features with low predictive
power either manually
or applying one of the many automated
feature selection methods
that are available at the command line
instead of manually tuning model
parameters
you can choose one of the optimizable
models and the app will try different
combinations of hyper parameter values
by using an optimization scheme that
seeks to minimize the classification
error
or cost the visualization shows how the
classification error decreases
as different combinations of hyper
parameters are evaluated
outside the app you can also let matlab
find the best optimized model
in a single step using fit c auto
after you create models interactively in
classification learner
you can export your best performing
model click export
the model should appear in your matlab
workspace and its predict method allows
you to make predictions on new data
you can also generate matlab code to
train your best model on new data
or integrate trained models into
computer vision signal processing
or data analytics applications to deploy
on embedded devices
automatically generate c-code for
predictions or
generate a software component from your
model for integration with standalone or
enterprise applications
when working with classification learner
help is always just a click away
simply navigate to the documentation
using the help button on the top right
to find all the information you need
for example the table here shows you
guidance on choosing the right
classifier
depending on accuracy speed and memory
constraints
that concludes a brief introduction to
classification learner
for more information click on the links
below thanks for watching