Scikit-Learn
3
Another option to use scikit-learn is to use
Python distributions like
Canopy
and
Anaconda
because they both ship the latest version of scikit-learn.
Features
Rather than focusing on loading, manipulating and summarising data, Scikit-learn library
is focused on modeling the data. Some of the most popular groups of models provided by
Sklearn are as follows:
Supervised Learning algorithms:
Almost all the
popular supervised learning
algorithms, like Linear Regression, Support Vector Machine (SVM), Decision Tree etc., are
the part of scikit-learn.
Unsupervised Learning algorithms:
On
the other hand,
it also has all the popular
unsupervised learning
algorithms from clustering,
factor analysis, PCA (Principal
Component Analysis) to unsupervised neural networks.
Clustering:
This model is used for grouping unlabeled data.
Cross Validation:
It is used to check the accuracy of supervised models on unseen data.
Dimensionality Reduction:
It is used for reducing the number of attributes in data which
can be further used for summarisation, visualisation and feature selection.
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