Thinking-data-science-a-data-science-practitioners-guide

12 1 Data Science Process
Fig. 1.8 Supervised/
unsupervised learning
Model Training
For both regression and classification models, the model training can be a supervised
learning or unsupervised learning, as seen in Fig. 1.8.
In case of supervised learning, you must have a labeled dataset, so to say that for
each data point in the dataset, the target value is known. Using this labeled dataset,
the model will tune its hyper-parameters and get ready to infer an unknown data
point. You may test the model’s accuracy on a validation dataset, which is part of
your original labeled dataset, but not used during training.
In case of unsupervised learning, you do not have labeled data, or rather, it is
impossible to create a labeled dataset because of its size. In such cases, you will use
those machine learning algorithms which will do the data analysis of your dataset on
their own. To cite an example here, an object detection model like OpenCV and
YOLO was trained using unsupervised learning.
Algorithm Selection
The major daunting task for a data scientist is to decide which algorithm to use. For
both regression and classification problems, there are many algorithms available in
our repositories. The challenge is to select the one which is most suitable for the
dataset and which can achieve a very high accuracy while predicting an unseen data
point. This book will help you understand several algorithms and select an appropriate
one for your application. To give you a quick overview of the several
algorithms available for machine learning, look at Fig. 1.9.
It is important for a data scientist to understand these algorithms, if not full
implementation, at least the concepts behind it. If you understand the algorithm
conceptually and its purpose, then you would know which one to use for your
current need. After all, there is somebody who has efficiently implemented these
algorithms, and we may not do a better job than those hard-cored developers. Even if
the implementation is not fully optimized, it is still okay in most cases. If your model
is doing real-time inferences, then only this high-level optimization would be called