Specify the training set as a list of rules between an input example and the output value:

Each example can contain a list of features:

Each example can contain an association of features:

Specify the training set as a list of rules between a list of inputs and a list of outputs:

Specify all the data in a matrix and mark the output column:

Specify all the data in a list of associations and mark the output key:

Specify all the data in a dataset and mark the output column:

Extract information from a trained predictor:

Get information about the input features:

Get the feature extractor used to process the input features:

Get a list of the supported properties:

Use the "Language" built-in classifier to detect the language in which a text is written:

Use it to detect the language of examples:

Obtain the probabilities for the most likely languages:

Restrict the classifier to some languages with the option ClassPriors:

Use the "FacebookTopic" built-in classifier to detect the topic of a Facebook post:

Classify multiple examples:

Unrecognized topics or languages will return Indeterminate:

Use the "CountryFlag" built-in classifier to recognize a country from its flag:

Use the "NameGender" built-in classifier to get the probable sex of a person from their first name:

Use the "NotablePerson" built-in classifier to determine what notable person is depicted in the given image:

Use the "Sentiment" built-in classifier to infer the sentiment of a social media message:

Use the "Profanity" built-in classifier to return True if a text contains strong language:

Use the "Spam" built-in classifier to detect if an email is spam from its content:

Use the "SpokenLanguage" built-in classifier to detect the language in which a text is written:

Create a classifier with an anomaly detector:

Change the value of the acceptance threshold when evaluating the classifier:

Permanently change the value of the acceptance threshold in the classifier:

Create a classifier and specify that an anomaly detector should be included:

Evaluate the classifier on an non-anomalous input:

Evaluate the classifier on an anomalous input:

The "Probabilities" property is not affected by the anomaly detector:

Temporarily remove the anomaly detector from the classifier:

Permanently remove the anomaly detector from the classifier:

Train a classifier on an imbalanced dataset:

The training example 5False is classified as True:

Classify this example with a uniform prior over classes instead of the imbalanced training prior:

The class priors can be specified during the training:

The class priors of a classifier can also be changed after training:

Train a FeatureExtractorFunction on a simple dataset:

Use the feature extractor function as a preprocessing step in Classify:

Train a classifier on texts preprocessed by custom functions and an extractor method:

Create a feature extractor and extract features from a dataset of texts:

Train a classifier on the extracted features:

Join the feature extractor to the classifier:

The classifier can now be used on the initial input type:

Train a classifier and give a name to each feature:

Use the association format to predict a new example:

The list format can still be used:

Train a classifier on a training set with named features and use FeatureNames to set their order:

Features are ordered as specified:

Classify a new example from a list:

Train a classifier on data where the feature is intended to be a sequence of tokens:

Classify wrongly assumed that examples contained two different text features:

The following classification will output an error message:

Force Classify to interpret the feature as a "NominalSequence":

Classify a new example:

Train a classifier with named features:

Both features have been considered numerical:

Specify that the feature "gender" should be considered nominal:

Specify a probability threshold when training the classifier:

Obtain class probabilities for an example:

As there are no class probabilities above 0.9, no prediction is made:

Specifying a threshold when classifying supersedes the trained threshold:

Update the value of the threshold in the classifier:

Train a logistic classifier:

Train a random forest classifier:

Plot the probability of class "a" given the feature for both classifiers:

Train a nearest neighbors classifier:

Find the classification accuracy on a test set:

In this example, using a naive Bayes classifier reduces the classification accuracy:

However, using a naive Bayes classifier reduces the classification time:

MONK's problems consist of synthetic binary classification datasets used for comparing the performance of different classifiers. Generate the dataset for the second MONK problem:

Test the accuracy of each available classifier by training on 169 examples and testing on the entire dataset:

Train a classifier with two input features:

Get class probabilities for an example that has a missing value:

Set the missing value synthesis to replace each missing variable with its estimated most likely value given known values (which is the default behavior):

Replace missing variables with random samples conditioned on known values:

Averaging over many random imputations is usually the best strategy and allows obtaining the uncertainty caused by the imputation:

Specify a learning method during training to control how the distribution of data is learned:

Classify an example with missing values using the "KernelDensityEstimation" distribution to condition values:

Provide an existing LearnedDistribution at training to use it when imputing missing values during training and later evaluations:

Specify an existing LearnedDistribution to synthesize missing values for an individual evaluation:

Control both the learning method and the evaluation strategy by passing an association at training:

Load the MNIST dataset:

Train a random forest classifier without any recalibration:

Visualize the calibration curve on a test set:

Train a random forest classifier with recalibration:

Visualize the calibration curve on a test set:

Train a classifier with an emphasis on training speed:

Compute the classification accuracy on a test set:

By default, a compromise between classification speed and performance is sought:

With the same data, train a classifier with an emphasis on training speed and memory:

The classifier uses less memory, but is also less accurate:

Train a classifier on the system's default GPU using a neural network and look at the AbsoluteTiming:

Compare the previous result with the one achieved by using the default CPU computation:

Train a classifier while specifying a total training time of 5 seconds:

Load the "Mushroom" dataset:

Train a classifier while specifying a target training time of 0.1 seconds:

The classifier reached an accuracy of about 90%:

Train a classifier while specifying a target training time of 5 seconds:

The classifier reached an accuracy of about 99%:

Load the "UCILetter" dataset:

Show training progress interactively during training of a classifier:

Show training progress interactively without plots:

Print training progress periodically during training:

Show a simple progress indicator:

Do not report progress:

Train a classifier:

By default, the most probable class is predicted:

This corresponds to the following utility specification:

Train a classifier that penalizes examples of class "yes" being misclassified as "no":

The classifier decision is different despite the probabilities being unchanged:

Specifying a utility function when classifying supersedes the utility function specified at training:

Update the value of the utility function in the classifier:

Train a logistic regression classifier on the Fisher iris data:

Obtain the L2 regularization coefficient of the trained classifier:

Specify a validation set:

A different L2 regularization coefficient has been selected:

Load the "Titanic" dataset, which contains a list of Titanic passengers with their age, sex, ticket class, and survival:

Visualize a sample of the dataset:

Train a logistic classifier on this dataset:

Calculate the survival probability of a 10-year-old girl traveling in third class:

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

Train a classifier to predict a person's odds of surviving or dying in the Titanic crash:

Calculate the prior odds of a passenger dying:

Use the classifier to predict the odds of a person dying:

Get an explanation of how each feature multiplied the model's predicted odds of a class:

Compare the model's explanation of feature impact to the base rate odds:

Train a classifier on the Fisher iris dataset to predict the species of Iris:

Predict the species of Iris from a list of features:

Test the accuracy of the classifier on a test set:

Generate a confusion matrix of the classifier on this test set:

Train a classifier that classifies movie review snippets as "positive" or "negative":

Classify an unseen movie review snippet:

Test the accuracy of the classifier on a test set:

Import examples of the writings of Shakespeare, Oscar Wilde, and Victor Hugo to train a classifier:

Generate an author classifier from these texts:

Find which author new texts are from:

Train a digit recognizer on 100 examples from the MNIST database of handwritten digits:

Use the classifier to recognize unseen digits:

Analyze probabilities of a misclassified example:

Train a classifier on 32 images of legendary creatures:

Use the classifier to recognize unseen creatures:

Train a classifier to recognize daytime from nighttime:

Test it on examples:

Import images of handwritten digits and select the 3s, 5s, and 8s:

Visualize a few of the images:

Convert the images into their pixel values and separate their class:

Train a classifier to identify the digit by its individual pixel values:

Learn a simple distribution of the data that treats each pixel as independent (for speed purposes):

Use the "SHAPValues" property to estimate how each pixel in an example impacted the predicted class:

Take the Log to convert the "odds multiplier" SHAP values onto a scale centered at 0:

Look at the impact of each pixel weighted by its darkness by multiplying by the pixel values:

Visualize how the pixels increased (red) or decreased (blue) the model's confidence the digit was a 0 or 6:

Train a classifier to flag suspicious transactions based on a set of features:

Plot the probability of fraud based only on the transaction amount:

Display the probability of fraud based on the card type and the transaction type: