KnnRegressor class abstract

A class that performs regression basing on k nearest neighbours algorithm

K nearest neighbours algorithm is an algorithm that is targeted to search most similar labelled observations (number of these observations equals k) for the given unlabelled one.

In order to make a prediction, or rather to set a label for a given new observation, labels of found k observations are being summed up and divided by k.

To get a more precise result, one may use weighted average of found labels - the farther a found observation from the target one, the lower the weight of the observation is. To obtain these weights one may use a kernel function.

Constructors

KnnRegressor(DataFrame fittingData, String targetName, int k, {KernelType kernel = KernelType.gaussian, Distance distance = Distance.euclidean, DType dtype = dTypeDefaultValue})
Parameters:
factory
KnnRegressor.fromJson(String json)
Restores previously fitted regressor instance from the given json
factory

Properties

distanceType → Distance
A distance type that is used to measure a distance between two observations
no setter
dtype → DType
A type for all numeric values using by the Predictor
no setterinherited
hashCode → int
The hash code for this object.
no setterinherited
k → int
A number of nearest neighbours
no setter
kernelType → KernelType
A kernel type
no setter
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
schemaVersion → int?
Contains a version of the current json schema
no setterinherited
targetNames → Iterable<String>
A collection of target column names of a dataset which was used to learn the ML model
no setterinherited

Methods

assess(DataFrame observations, MetricType metricType) → double
Assesses model performance according to provided metricType
inherited
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
predict(DataFrame testFeatures) → DataFrame
Returns prediction, based on the learned coefficients
inherited
retrain(DataFrame data) → KnnRegressor
Re-runs the learning process on the new training data. The features, model algorithm, and hyperparameters remain the same.
inherited
saveAsJson(String filePath) → Future<File>
Saves a json-serializable map into a newly created file with the path filePath
inherited
toJson() → Map<String, dynamic>
Returns a json-serializable map
inherited
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited