scikit-learn was initially created in 2007 as a way to easily create machine learning models in the Python programming language by David Cournapeau. Since its inception, the library has grown immensely in popularity because of its ease of use, wide adoption within the machine learning community, and flexibility of use.
In the next section are a few of the advantages and disadvantages of using scikit-learn for machine learning purposes.
Advantages of scikit-learn are as follows:
Mature: scikit-learn is well established within the community and used by members of the community of all skill levels. The package includes most of the common machine learning algorithms for classification, regression, and clustering tasks.
User-friendly: scikit-learn features an easy-to-use API that enables beginners to efficiently prototype without having to have a deep understanding or code each specific mode.
Open source: There is an active open source community working to improve the library, add documentation, and release regular updates, which ensures that the package is stable and up to date.
Disadvantage of scikit-learn is as follows:
Neural network support lacking: Estimators with ANN algorithms are minimal.
The estimators in scikit-learn can generally be classified into supervised learning and unsupervised learning techniques. Supervised learning occurs when a target variable is present. A target variable is a variable of the dataset for which you are trying to predict given the other variables. Supervised learning requires the target variable to be known and models are trained in order to correctly predict this variable. Binary classification using logistic regression is a good example of a supervised learning technique.
In unsupervised learning, there is no target variable given in the training data, but models aim to assign a target variable. An example of an unsupervised learning technique is k-means clustering. This algorithm partitions data into a given number of clusters based on proximity to neighboring data points. The target variable assigned may be either the cluster number or cluster center.
An example of utilizing a clustering example in practice may look as follows. Imagine that you are a jacket manufacturer and your goal is to develop dimensions for various jacket sizes. You cannot create a custom-fit jacket for each customer, so one option you have to determine the dimensions for jackets is to sample the population of customers for various parameters that may be correlated to fit, such as height and weight. Then, you can group the population into clusters using scikit-learn's k-means clustering algorithm with a cluster number that matches with the number of jacket sizes you wish to produce. The cluster-centers that are created from the clustering algorithm become the parameters on which the jacket sizes are based. This is visualized in the following figure:
Figure 1.24: An unsupervised learning example of grouping customer parameters into clusters
There are even semi-supervised learning techniques in which unlabeled data is used in the training of machine learning models. This technique may be used if there is only a small amount of labeled data and a copious amount of unlabeled data. In practice, semi-supervised learning produces a significant improvement in model performance compared to unsupervised learning.
The scikit-learn library is ideal for beginners as the general concepts for building machine learning pipelines can be learned easily. Concepts such as data preprocessing, hyperparameter tuning, model evaluation, and many more are all included in the library. Even experienced users find the library easy to rapidly prototype models before using a more specialized machine learning library.
Indeed, the various machine learning techniques discussed such as supervised and unsupervised learning can be applied with Keras using neural networks with different architectures that will be discussed throughout the book.
