Predicting with a model

by: Kevin Broløs & Chris Cave
(Feyn version 2.0 or newer)

A Model that has been fitted can be used to perform predictions on a dataset.

An example model that adds two inputs together could look like this:

A regressor outputs values within the range of the output variable.

A binary classifier outputs values between 0 and 1. These are the probabilities of the samples belonging to the positive class. This can be rounded to get a discrete prediction of 0 or 1.

The data you want to predict for should as a minimum contain the inputs present in the Model. Any additional columns present will be ignored.

Here is an example of using the predict function.

import feyn
from feyn.datasets import make_classification

# Generate a dataset and put it into a dataframe
train, test = make_classification()

# Connect to a QLattice and run a classification simulation
ql = feyn.connect_qlattice()
models = ql.auto_run(
    data=train,
    output_name='y',
    kind='classification'
)

# Select the best model and predict
best = models[0]

best.predict(test)

The predict function returns a np.array of predictions.

For guides on how to evaluate a model, check out regression plot for regressors and ROC curve for classifiers.