深入瞭解 Microsoft.ML.SamplesUtils 命名空間中的 Microsoft.ML.SamplesUtils.DatasetUtils.MulticlassClassificationExample。
用于生成随机 DatasetUtils.MulticlassClassificationExample 对象的帮助程序函数。 C# 复制 public static System.Collections.Generic.List<Microsoft.ML.SamplesUtils.DatasetUtils.MulticlassClassificationExample> GenerateRandomMulticlassClassificationExamples (int count); 参数 count Int32 生成的示例数...
In this classification, one target label is assigned to each sample, but the sample cannot have two or more labels at the same time [36]. For example, an animal can be a dog or a cat, not both at the same time [37]. 3. Multilabel classification: The multilabel classification ...
Binary Classification Problem 1: red vs [blue, green] Binary Classification Problem 2: blue vs [red, green] Binary Classification Problem 3: green vs [red, blue] A possible downside of this approach is that it requires one model to be created for each class. For example, three classes req...
This is an example illustrating the use of the multiclass classification tools from the dlib C++ Library. Specifically, this example will make points from three classes and show you how to train a multiclass classifier to recognize these three classes. ...
Let's start by creating a dataset to train a multiclass classification model. 1 2 3 # make dataset for example centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]] X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30) The Obvious ...
Description When using lgb.DaskLGBMClassifier with multiclass classification the same split produces different numbers of samples being sent to each child. Reproducible example import dask.array as da import lightgbm as lgb import numpy ...
Any customizations must be done in the binary classification model that is provided as input. Add a binary classification model to the experiment, and configure that model. For example, you might use a Two-Class Support Vector Machine or Two-Class Boosted Decision Tree. If you need help ...
The IEstimator<TTransformer> to predict a target using a linear multiclass classifier model trained with a coordinate descent method. Depending on the used loss function, the trained model can be, for example, maximum entropy classifier or multi-cl
On the canvas, right-click the PS-SMART Multiclass Classification component and choose View Data > View Output Port 3 to view the feature importance result. Parameters: id: the ID of a passed feature. In this example, the input data is in the key-value format. The values in the id ...