Sklearn library comes loaded with a lot of features such as : Supervised Learning : Here we are importing iris dataset in sklearn Features of scikit-learn The sklearn.datasets package includes some small toy datasets that also helps to fetch larger datasets that do not require downloading any file from some external websites. Suppose you want to import datasets function in the sklearn library you can follow the below command : Loading dataset Instead of the whole library, if you want to import a specific function in it you can also do this by using from – import method. Here, import the “sklearn” library using the alias “sk”. We can also import the python library using the alias. An import statement is created by the import keyword along with the name of the library. To make use of the functions in a library, you’ll need to import the library with the help of an import statement. To upgrade the installed library we can use a command Importing of a library You can also install sklearn with required version by using command : Upgrading a library If you want to install the sklearn library directly using Jupyter notebook or Google colab then use following command : Install sklearn with required version There are multiple ways to install the sklearn library, let’s see how to install it : pip command : conda command : Using Google colab and Jupyter notebook : It should not be used for reading the data, manipulating data and summarizing data. Sklearn Is Used To Build Machine Learning Models. Sklearn library comes loaded with a lot of features such as classification, regression, clustering and dimensionality reduction algorithms include k-means, K-nearest neighbors, Support Vector Machines (SVM), Decision Trees also supports python numerical and scientific libraries NumPy and SciPy. It’s a free and the most useful machine learning library for Python.
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