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This article is about how to use make_blobs in sklearn. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

The make_blobs function in sklearn is mainly used to generate datasets, as shown below:

1. The usage of calling make_blobsfrom sklearn.datasets import make_blobs2.make_blobs data, label = make_blobs (n_features=2, n_samples=100, centers=3, random_state=3, cluster_std= [0.8,2,5])

N_features indicates how many eigenvalues are in each sample.

N_samples indicates the number of samples

Centers is the number of cluster centers, which can be understood as the number of species of label.

Random_state is a random seed, which can fix the generated data.

Cluster_std sets the variance for each category

The following examples are given:

'' create a training dataset''from sklearn.datasets import make_blobsdata, label = make_blobs (n_features=2, n_samples=100, centers=2, random_state=2019, cluster_std= [0.6, 0.7])

Take a look at the generated dataset:

Data has 2 features (n_features=2), and the number of samples is 100 (n_samples=100).

Then take a look at the generated label:

Label has only 0 or 1 (centers=2), and the dimension is 100

Random_state given a value, each generated dataset is fixed, convenient for later reproduction, the default is each random generation, should pay attention to!

All right, so we have a dataset we want, and then we can start some follow-up work!

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