Tutorial by Examples

The current stable version of scikit-learn requires: Python (>= 2.6 or >= 3.3), NumPy (>= 1.6.1), SciPy (>= 0.9). For most installation pip python package manager can install python and all of its dependencies: pip install scikit-learn However for linux systems it is recomm...

Using iris dataset: import sklearn.datasets iris_dataset = sklearn.datasets.load_iris() X, y = iris_dataset['data'], iris_dataset['target'] Data is split into train and test sets. To do this we use the train_test_split utility function to split both X and y (data and target vectors) randomly w...

Finding patterns in data often proceeds in a chain of data-processing steps, e.g., feature selection, normalization, and classification. In sklearn, a pipeline of stages is used for this. For example, the following code shows a pipeline consisting of two stages. The first scales the features, and t...

Different operations with data are done using special classes. Most of the classes belong to one of the following groups: classification algorithms (derived from sklearn.base.ClassifierMixin) to solve classification problems regression algorithms (derived from sklearn.base.RegressorMixin) to so...

For ease of testing, sklearn provides some built-in datasets in sklearn.datasets module. For example, let's load Fisher's iris dataset: import sklearn.datasets iris_dataset = sklearn.datasets.load_iris() iris_dataset.keys() ['target_names', 'data', 'target', 'DESCR', 'feature_names'] You can ...