Create an artificial dataset from three normally distributed clusters:

Plot the dataset:

The training data consists of rules mapping the point to the cluster it is in:

Create a net to compute the probability of a point lying in each cluster, using a "Class" decoder to classify the input as Red, Green or Blue:

Train the net on the data:

Evaluate the net on the centers of each cluster:

Show the contours in feature space in which each class reaches a posterior probability of 0.75:

Plot the degree of uncertainty of the classifier as measured by entropy as a function of the position:

Perform logistic regression on the Fisher Iris dataset. First, obtain the training data:

Build a list of the unique labels to which each example is assigned:

Create a NetChain to perform the classification, using a "Class" decoder to interpret the output of the net as probabilities for each class:

NetTrain will automatically use a CrossEntropyLossLayer with a NetEncoder to interpret the class labels in the training data:

Classify an input:

Obtain the probabilities associated with the classification:

Use NetMeasurements to test the classification performance of the trained net on the test set:

First, obtain the training data, and drop rows containing missing data:

Split the data into training and test datasets:

Create "Class" encoders that encode the categorical variables as one-hot encoded vectors:

Applying the encoders to class labels produces unit vectors:

The input features are first concatenated together before being further processed:

Train the net on the training data. NetTrain will automatically attach a CrossEntropyLossLayer["Binary"] layer to the output of the net:

Predict whether a passenger will survive:

Obtain the probability of survival associated with the input:

Plot the survival probability as a function of age for some combinations of "class" and "sex":

Use NetMeasurements to test the accuracy of the trained net on the test set:

The accuracy is typically comparable to that obtained using Classify when specifying the method "LogisticRegression":

First, obtain training data:

The training data consists of an image and the corresponding high-level and low-level labels:

Extract the unique labels from the "Label" and "SubLabel" columns:

Create a base convolutional net that will produce a vector of 500 features:

Create a NetGraph that will produce separate classifications for the high-level and low-level labels:

Train the network. NetTrain will automatically attach CrossEntropyLossLayer objects to both outputs, taking the target values from the training data using the corresponding names "Label" and "SubLabel":

Evaluate the trained network on a single image:

Evaluate the trained network on several images, taking only the "SubLabel" output:

Get a property of the class decoder of the "SubLabel" output on an input image:

From a random sample, select the images for which the net produces highest and lowest entropy predictions for "Label":

Use NetMeasurements to test the accuracy for both outputs of the net:

Produce a subnetwork that computes only "SubLabel" predictions:

Make a prediction on a single image: