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

Each example can contain a list of features:

Each example can contain an association of features:

Specify the training set a list of rule between a list of input and a list of output:

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

Create a predictor with an anomaly detector:

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

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

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

Evaluate the predictor on a non-anomalous input:

Evaluate the predictor on an anomalous input:

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

Temporarily remove the anomaly detector from the predictor:

Permanently remove the anomaly detector from the predictor:

Generate a predictor function using FeatureExtractor to preprocess the data using a custom function:

Add the "StandardizedVector" method to the preprocessing pipeline:

Use the predictor on new data:

Create a feature extractor and extract features from a dataset:

Train a predictor on the extracted features:

Join the feature extractor to the predictor:

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

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

Features are ordered as specified:

Predict a new example from a list:

Train a predictor on textual and nominal data:

The first feature has been wrongly interpreted as a nominal feature:

Specify that the first feature should be considered textual:

Predict a new example:

Train a predictor with named features:

Both features have been considered numerical:

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

Specify a probability density threshold when training the predictor:

Visualize the probability density for a given example:

As no value has a probability density above 0.5, no prediction is made:

Specifying a threshold when predicting supersedes the trained threshold:

Update the value of the threshold in the predictor:

Train a linear predictor:

Train a nearest-neighbors predictor:

Plot the predicted value as a function of the feature for both predictors:

Train a random forest predictor:

Find the standard deviation of the residuals on a test set:

In this example, using a linear regression predictor increases the standard deviation of the residuals:

However, using a linear regression predictor reduces the training time:

Train a linear regression, neural network, and Gaussian process predictor:

These methods produce smooth predictors:

Train a random forest and nearest-neighbor predictor:

These methods produce non-smooth predictors:

Train a neural network, a random forest, and a Gaussian process predictor:

The Gaussian process predictor is smooth and handles small datasets well:

Train a predictor with two input features:

Get the prediction 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:

Predict 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:

Train a predictor with an emphasis on training speed:

Find the standard deviation of the residuals on a test set:

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

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

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

Load the Boston Homes dataset:

Train a predictor with model calibration:

Visualize the comparison plot on a test set:

Remove the recalibration function from the predictor:

Visualize the new comparison plot:

Train a predictor 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 predictor while specifying a total training time of 3 seconds:

Load the "BostonHomes" dataset:

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

The predictor reached a standard deviation of about 3.2:

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

The standard deviation of the predictor is now around 2.7:

Load the "WineQuality" dataset:

Show training progress interactively during training of a predictor:

Show training progress interactively without plots:

Print training progress periodically during training:

Show a simple progress indicator:

Do not report progress:

Train a predictor:

Visualize the probability density for a given example:

By default, the value with the highest probability density is predicted:

This corresponds to a Dirac delta utility function:

Define a utility function that penalizes the predicted value's being smaller than the actual value:

Plot this function for a given actual value:

Train a predictor with this utility function:

The predictor decision is now changed despite the probability density's being unchanged:

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

Update the predictor utility:

Visualize the distribution of age for the name "Claire" with the built-in predictor "NameAge":

The most likely value of this distribution is the following:

Change the utility function to predict the mean value instead of the most likely value:

Train a linear regression predictor on the "WineQuality" data:

Obtain the L2 regularization coefficient of the trained predictor:

Specify a validation set:

A different L2 regularization coefficient has been selected:

Train a predictor that predicts the median value of properties in a neighborhood of Boston, given some features of the neighborhood:

Generate a PredictorMeasurementsObject to analyze the performance of the predictor on a test set:

Visualize a scatter plot of the values of the test set as a function of the predicted values:

Compute the root mean square of the residuals:

Load a dataset of the average monthly temperature as a function of the city, the year, and the month:

Visualize a sample of the dataset:

Train a linear predictor on the dataset:

Plot the predicted temperature distribution of the city "Lincoln" in 2020 for different months:

For every month, plot the predicted temperature and its error bar (standard deviation):

Load a dataset of wine quality as a function of the wines' physical properties:

Visualize a few data points:

Get a description of the variables in the dataset:

Visualize the distribution of the "alcohol" and "pH" variables:

Train a predictor on the training set:

Predict the quality of an unknown wine:

Create a function that predicts the quality of the unknown wine as a function of its pH and alcohol level:

Plot this function to have a hint on how to improve this wine:

Load a dataset of wine quality as a function of the wines' physical properties:

Train a predictor to estimate wine quality:

Examine an example bottle:

Predict the example bottle's quality:

Calculate how much higher or lower this bottle's predicted quality is than the mean:

Get an estimation for how much each feature impacted the predictor's output for this bottle:

Visualize these feature impacts:

Confirm that the Shapley values fully explain the predicted quality:

Learn a distribution of the data that treats each feature as independent:

Estimate SHAP value feature importance for 100 bottles of wine, using 5 samples for each estimation:

Calculate how important each feature is to the model:

Visualize the model's feature importance:

Visualize a nonlinear relationship between a feature's value and its impact on the model's prediction:

Generate images of gauges associated with their values:

Train a predictor on this dataset:

Predict the value of a gauge from its image:

Interact with the predictor using Dynamic:

Import a dataset with data about customer purchases:

Train a "GradientBoostedTrees" model to predict the total spending based on the other features:

Use the model to predict the most likely spending by location:

Visualize the data on a map:

For the top three locations, estimate the spending amount as a function of the customer age:

Define an year range:

Compute the model predictions:

Create the dataset to plot:

Visualize it: